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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

Continuation and convergence for the higher dimensional conformal Chern--Calabi flow

We study the volume-normalized gradient flow of the squared Chern scalar curvature on compact Hermitian manifolds of complex dimension $n\ge3$. The flow extends smoothly past a finite time if the spatial supremum of Chern scalar curvature belongs to $L^2$ in time. A uniform curvature bound in the evolving $L^p$ norm, for one fixed $p>n$, gives global existence and smooth convergence of the whole trajectory to a constrained critical metric. On Chern scalar-flat backgrounds, small initial $L^p$ curvature gives exponential convergence to the background. A corresponding condition on the trace-free first Chern--Ricci form gives exponential convergence on Chern--Ricci-flat Hermitian and positive Kähler--Einstein backgrounds. The smallness thresholds are independent of the initial higher derivatives. We also construct constant Chern scalar curvature backgrounds of either sign with arbitrarily small smooth perturbations whose solutions, if global, cannot converge back to the background.

math.CV↗

arXiv2026

Numerical reconstruction of polygonal obstacles by diffracted waves

We present a numerical method for recovering rigid polygonal obstacles in two dimensions, based on the diffraction-based inverse scattering theory of Bao, Chen, Lu, and Xiong. A single pulse is emitted from a point source, and the resulting wave field is measured on a receiver curve. After isolating the diffracted signals, we recover the visible vertices from arrival times and the corner orientations and opening angles from amplitudes. The use of amplitude information permits local reconstruction in the reflection shadow, where reflected waves do not reach the receiver. The amplitudes also determine the direction of a shadowed edge adjacent to a visible vertex, enabling local non-line-of-sight imaging. Numerical experiments for a convex corner, a concave corner, a triangle, a pentagon and a star-shaped obstacle confirm that the visible portion of the boundary can be recovered stably from diffracted signals. A further convex-corner test with additive noise at level $0.2\%$ on the single arc $L_1$ confirms that the tip and opening remain recoverable.

math.NA↗

arXiv2026

Memory-Aware Reward Modeling: Learning Human Preferences for LLM Memory Use

Memory enables language model assistants to maintain continuity across sessions and generate personalized responses, but knowing more about a user does not necessarily produce responses they prefer. Existing evaluations assess memory capabilities without directly establishing whether memory use benefits the user. To study this gap, we collect approximately 1,000 pairwise judgments from 87 participants using their own ChatGPT queries and personal memories. We find that memory-conditioned responses are not consistently preferred over responses generated without memory and in some cases, users strongly prefer the latter. Automatic evaluators also show limited agreement with users and favor memory-conditioned responses substantially more often than users do. Motivated by these gaps, we introduce a memory-aware reward model that learns from user judgments, achieving 72.8\% preference prediction accuracy and outperforming the existing baselines. Our findings motivate evaluation and optimization objectives that align memory use with the preferences of the people whose information is being used.

cs.CL↗

arXiv2026

Hardness and Approximation for Compressing Deterministic Finite Automata

The delayed deterministic finite automaton ($\text{D}^2\text{FA}$) is a powerful tool for compressing the prohibitively large deterministic finite automata (DFAs) used in modern signature-based deep packet inspection systems. A $\text{D}^2\text{FA}$ augments a DFA with unlabeled default transitions to remove transitions that are similar. This technique works well in practice and has inspired numerous subsequent studies. However, existing approaches do not guarantee a $\text{D}^2\text{FA}$ of optimal size as they restrict the default transitions to form a forest. In this paper, we consider the problem of finding an optimal $\text{D}^2\text{FA}$. We make the key observation that cycles of default transitions can be valid and produce a smaller $\text{D}^2\text{FA}$, provided that for every character in the alphabet, at least one state in the cycle has an outgoing transition labeled by that character. This observation raises fundamental questions concerning the computational complexity of finding an optimal $\text{D}^2\text{FA}$ and the approximation guarantee of the standard (forest-restricted) approach. We answer both questions by showing that it is NP-hard to find an optimal $\text{D}^2\text{FA}$ and by proving that the standard approach provides a $3/2$-approximation to the optimum. To establish NP-hardness, we construct a reduction from set cover using a DFA with gadgets where valid default transition choices correspond to selecting sets. To prove our approximation ratio, we compare the cycles in an optimal solution to a variant where the default transitions form a forest. Finally, we demonstrate that this $3/2$ ratio is tight via a constructed family of DFAs.

cs.DS↗

arXiv2026

PointerBench: A Diagnostic Benchmark for Fine-Grained GUI Grounding

Multimodal Large Language Models have shown promising performance in computer-use tasks, from planning user workflows to grounding natural-language actions to precise UI targets. Existing end-to-end benchmarks primarily emphasize long-horizon task completion, while GUI grounding benchmarks remain focused on conventional controls and a limited set of professional interfaces. We introduce POINTERBENCH, a diagnostic suite of 1,500 screenshot-instruction pairs probing three complementary capabilities: thin sub-element identification and geometric grounding in spreadsheets (POINTERBENCH-Sheets), character-level grounding in long-text documents (POINTERBENCH-Text), and mixed GUI grounding across 100 professional applications (POINTERBENCH-Pro). We carefully curate each task with pixel-exact target geometry and evaluate both general-purpose multimodal models and 39 specialist GUI grounding configurations. We find text grounding to be the most challenging setting, with Claude Fable 5, GPT-5.6 Sol, and the leading native specialist UI-Venus-2-9B achieving 69.6%, 53.4%, and 25.8% success on POINTERBENCH-Text despite respective 83.2%, 77.7%, and 54.5% average success across POINTERBENCH. These results reveal substantial headroom in fine-grained GUI grounding across frontier and specialist models. We publicly release the benchmark and evaluation code at https://github.com/warmwindOS/pointerbench.

cs.AI↗

arXiv2026

Learning Robot Behavior Priors from Non-Robotic Synthetic Data

Real robot data, rich across tasks and morphologies, is often treated as the bottleneck for training robot learning models, including embodied foundation models that aim for zero-shot physical transfer. In this paper, we investigate the degree to which such models can acquire necessary structure using data from systems without embodiment. To this end, we test if inexpensive, procedurally generated synthetic data can yield useful priors before a model observes any real-world behavior. We study this through robot action prediction, a natural pretraining objective and a direct test of whether structure learned from arbitrary synthetic tasks transfers to embodied settings. We introduce three data generation pipelines that model mathematical abstractions of robotic control, and use them as pretraining priors for a Transformer. We demonstrate that a model trained purely on this non-robotic data attains non-trivial zero-shot prediction performance on unseen physical robot tasks via an in-context learning mechanism, and that our specialized control priors notably improve upon generic causal priors. We also compare against a compute-matched baseline trained on real robot data, and find that it outperforms our model but only on the datasets it was trained on. These findings suggest that procedurally generated, mathematical abstractions can yield useful robotic priors, offering a potential complement to expensive embodied data collection.

cs.RO↗

arXiv2026

RIPPLE: Recovery Internalization through Process-Level Policy Learning and Effect-Aware Credit for Long-Horizon Agentic Coding

Long-horizon coding agents can observe a failure and generate a plausible critique yet continue along the same incorrect trajectory. We call this the Critique-to-Action Handoff Gap. We introduce RIPPLE, a recovery-oriented training framework that internalizes process-level guidance into a single self-recovering policy. A prefix-only teacher provides evidence-grounded recovery supervision without access to future actions or terminal outcomes. After lightweight distillation, reinforcement learning assigns stage-specific credit to reflection triggering, diagnosis, and corrective action. Local validity signals gate trajectory advantages so that intermediate recovery decisions do not indiscriminately inherit terminal outcomes. Across four repository-level software-engineering benchmarks, RIPPLE improves the base policy by an arithmetic mean of 4.8 score points, with no auxiliary critic at deployment. Controlled ablations isolate the benefits of local, stage-aligned credit, and matched-prefix interventions show that critique semantics affect subsequent correction. Recovery gains concentrate where the prefix contains sufficient diagnostic evidence and the repository state remains reversible within the remaining budget.

cs.AI↗

arXiv2026

CoWriter: Guiding Methodological and Synergetic Human-LLM Co-Authoring

While LLMs have transformed text generation, standard chat-and-canvas interfaces often reduce human authoring to quite passive supervision, causing review fatigue, over-reliance on AI, and the sidelining of the human role in long-form, source-grounded writing. Prior systems typically address isolated writing phases, leaving comprehensive end-to-end human-LLM collaboration largely unexplored. In this paper, we introduce CoWriter, a proactive agentic scheme that adapts expert human (co-)authoring methodologies from cognitive science to flexibly steer the entire writing workflow while actively encouraging the user to engage. In an exploratory user study, compared to a standard LLM baseline, authors using CoWriter better expressed their perspectives and maintained a higher sense of document ownership, supported quantitatively by $2.2\times$ more successful agent engagements. Furthermore, external reader judges rated the resulting texts higher in "human touch" qualities like storytelling and integrating unique personal experiences. Overall, our results demonstrate that proactively engaging users through established human practices is beneficial for effective LLM-supported co-authoring, providing useful methodologies, techniques and insights for future research on collaborative agents.

cs.CL↗

arXiv2026

Optimal weighted sampling of positive and normal operator orbits

We study weighted sampling of operator orbit frames in infinite dimensional separable Hilbert spaces $H$, keeping the operator and generating vectors fixed. Let $N_μ(R)$ count the sampling times in $[0,R]$. For a single orbit of a positive operator, let $m_A(R)$ count the eigenvalues in $(0,e^{-1/R}]$. Every real sampling design that preserves the frame property must satisfy the lower bound $N_μ(R)\ge m_A(R)-O_μ(1)$. Conversely, for every $η>0$, we construct an integer sampling design that preserves the frame property and satisfies $N_μ(R)\le(1+η)m_A(R)+O_η(1)$. Thus the coefficient $1$ is optimal. Under additional spectral conditions, we attain $N_μ(R)=m_A(R)+O(1)$ and characterize the weights that preserve the frame property on the constructed sets. For finitely or countably many orbits of a positive operator, let $P_j$, $j\ge1$, be the spectral projection corresponding to $2^{-j-1}<-\logλ\le2^{-j}$ and define $K_A(R)=\#\{j\ge1:2^j\le R,\ P_j\ne0\}$. Every real sampling design that preserves the frame property satisfies $N_μ(R)\ge c_μK_A(R)-C_μ$ for $R\ge2$, with constants $c_μ>0$ and $C_μ\ge0$. Conversely, we construct an integer sampling design that preserves the frame property and satisfies $N_μ(R)\le L K_A(R)+L$ for $R\ge2$, where $L$ depends only on the bounds of the given orbit frame. For normal operators of norm one, we prove that the smallest frame condition number is attained among integer sampling designs that preserve the frame property and satisfy a prescribed counting bound, whenever this class is nonempty. Finally, we construct one normal operator with two families of generating vectors for which the optimal growth orders of $N_μ(R)$ are $\log R$ and $R$, respectively, for both real and integer sampling.

math.FA↗

arXiv2026

Three-orbifolds, positive scalar curvature and fundamental group

We show that closed Riemannian 3-orbifolds with positive scalar curvature have virtually free fundamental group. We also build a family of closed 3-orbifolds with free fundamental group of rank 2 which do not admit Riemannian metrics of positive scalar curvature.

math.DG↗

arXiv2026

Beyond Accuracy: Disentangling Decomposition from Typed-State Quality Assurance in Multi-Agent LLM Pipelines

Multi-agent LLM pipelines are often credited with gains in software engineering tasks, but it is rarely clear whether a gain stems from the typed contract between agents or from the specialized prompts that decomposition enables. We study this attribution problem in Requirements Engineering, comparing three architectures for classifying requirements as functional or non-functional and then prioritizing them with MoSCoW: a single-call baseline; a two-call variant that reuses the pipeline's specialized prompts but passes an unvalidated intermediate; and a pipeline with a Pydantic-validated typed state. The 18 conditions cover PROMISE NFR+ (625 requirements), three locally hosted small language models, and two languages. The F1 gain over the baseline is associated with the two-call, specialized-prompt variant (up to +0.075), whereas the typed state adds a small difference (-0.003 to +0.013) that is significant in one of six comparisons. Beyond accuracy, the typed pipeline contains out-of-range model outputs at the agent boundary, while the unvalidated variant fails later and drops the requirement, and it shows a small, reproducible increase in inter-model MoSCoW consistency whose cause our design cannot isolate. The study is associative: the contrasts bundle prompts, call count, and staged context, and the results come from three 7--8B models on one dataset. The implementation and replication package are archived on Zenodo (https://doi.org/10.5281/zenodo.23380444).

cs.SE↗

arXiv2026

Dyon Condensation at $θ=2π$ from Wilson-'t Hooft Loops in Yang-Mills Theory

Although Yang--Mills theories at $θ$ and $θ+2π$ are physically equivalent, their periodicity is realized nontrivially in the presence of a background field for the $1$-form center symmetry. The confining vacua at $θ=0$ and $2π$ are therefore expected to have distinct symmetry-protected topological labels. We test this expectation in $SU(2)$ lattice Yang--Mills theory by measuring 't~Hooft and dyonic Wilson--'t~Hooft loops at $θ=2π$. A $1$-form-covariant DBW2 flow makes the global reweighting factor numerically benign while retaining the physical non-integer topological charge in a defect ensemble. The dyonic loop exhibits a clear perimeter-law plateau on two lattice volumes. The direct 't~Hooft-loop signal is noise-limited but has the magnitude and distance dependence expected for an area law. Taken together with the Wilson--'t~Hooft classification, these results provide numerical evidence for the dyonic oblique-confinement pattern at $θ=2π$. This talk is based on our recent paper (arXiv:2606.13428).

hep-lat↗

arXiv2026

ReCoG: Relational Concept Graph for Training-Free Personalization

Training-free personalization adapts a frozen vision-language model (VLM) to recognize user-defined concepts from only a few exemplars by retrieving concept entries at inference time. While recent retriever-based methods enrich each concept with descriptive evidence, they largely treat concepts in isolation, so the retrieved information can be plausible yet non-discriminative, especially when the personalized concept set contains highly confusable, fine-grained categories. We argue that personalization is inherently relational: correct recognition depends on the specific distinctions between a concept and its nearest alternatives. To this end, we propose ReCoG, a Relational Concept Graph for retrieval-augmented personalization. ReCoG represents concepts as nodes and augments them with directed edges that explicitly capture discriminative cues between concept pairs, elicited by a frozen VLM during database construction. At inference, ReCoG retrieves a shortlist and resolves ambiguity via explicit pairwise comparisons conditioned on the corresponding relational edges, selecting the concept that consistently wins against competing candidates. We also introduce FINGER benchmark to test personalized recognition on confusable concept sets. Across FINGER, ReCoG substantially outperforms prior methods, with the largest gains in the most confusable regimes. ReCoG also achieves consistently best performance on established personalization benchmarks including captioning and VQA, confirming that relational evidence is broadly beneficial beyond fine-grained settings. Project Page: https://csy-98.github.io/ReCoG/

cs.CV↗

arXiv2026

High-fidelity entanglement of two spectrally-distinct solid state emitters

Solid state quantum emitters with electronic spin properties can be entangled using optical interferometric which-path erasure techniques that rely on identical frequencies and linewidths of the emitters. This promises a powerful, fast and scalable platform for quantum technologies. Using current protocols, a pair of emitters embedded in a waveguide interferometer can be deterministically prepared in a maximally entangled state. However, the amount of entanglement diminishes rapidly with increasing separation of the emission lines and variations in the linewidths of the emitters. Solid state emitters such as quantum dots are subject to intrinsic variations in the fabrication process. Therefore, manufacturing the required number of identical emitters on a chip is not scalable. In this Letter, we present a method that uses readily available pairs of entangled photons to entangle solid state emitters with large transition frequency separations and linewidth variations, circumventing the need to fabricate optically identical emitters. We demonstrate a concurrence of C = 0.95 for typical quantum dots using narrowband photon inputs.

quant-ph↗

arXiv2026

Retrieval-Augmented Online Textual Memory for Episodic Memory Video Question Answering

Long egocentric videos pose a central challenge for multimodal language models: the visual stream must be compressed into a memory representation that can be searched efficiently while still preserving the evidence needed for question answering. Building on an online textual-memory framework in which first-person videos are converted into temporally grounded captions, this work systematically studies the role of retrieval in episodic-memory video question answering. We evaluate how different retrieval units, query formulations, context budgets, and reranking strategies affect multiple-choice answering on QAEgo4D-Closed, isolating retrieval as the main variable while keeping the underlying generated memory fixed. Our best configuration, Parent, matches fine-grained caption subchunks, then provides their full parent captions to the reasoner. It achieves 56.4 % accuracy, matching strong full-memory and retrieval baselines while exposing only a small fraction of the textual memory at question time. More importantly, our analysis shows that better temporal coverage does not necessarily translate into better answers: increasing the retrieval budget improves temporal Hit@K and Recall@K, but can reduce accuracy by introducing visually plausible distractors and uncertain caption evidence. These findings suggest that retrieval-augmented egocentric memory should not be optimized only for evidence recall, but also for the reliability, specificity, and answer-discriminative value of the retrieved context. The study motivates more structured episodic memories that move beyond raw caption retrieval toward evidence-aware representations for long-form egocentric reasoning.

cs.CV↗

arXiv2026

Analysis of a coronal hole jet in the chromosphere and the transition region with IRIS data and SDO observations

Coronal jets are transient phenomena that occur throughout the solar atmosphere, often triggered by the activation of mini-filaments. Jets are an important field of study, as they are believed to contribute to the mass supply of the solar wind. We study a small-scale solar jet occurring in a low-latitude coronal hole, focusing on traces of the jet eruption to the lower atmosphere, utilizing chromospheric and transition region diagnostics. We also investigate the possible mechanisms and dynamic conditions leading to its onset. We analyze ultraviolet spectra from the Interface Region Imaging Spectrograph to investigate chromospheric dynamics and their connection to the transition region, using diagnostics of the Mg II k 2796.35 Å and Si IV 1393.75 Å lines, together with non-LTE inversions. We complement this analysis with multiwavelength imaging and magnetograms from the Solar Dynamics Observatory, as well as magnetic field modeling. The analysis of Mg II k and Si IV parameters reveals a coupling between chromospheric and transition region plasma dynamics. Profiles on the jet spire present deeper central reversals, larger peak separations and higher transition region Doppler velocities. The jet initiation is associated with a mini-filament-like structure, preceded by dynamic variations in the photospheric magnetic field. We find single-peaked Mg II k profiles at one of its footpoints, possibly indicating plasma drainage. The presence of a null point and a quasi-separatrix layer suggests that reconnection can play a role in the mini-filament-like structure destabilization and jet eruption. This study suggests that the signature and possibly the origin of coronal jets can be traced down to the chromosphere. The detailed analysis of the Mg II k profiles at the jet spire and the mini-filament-like structure may provide valuable observational constraints for future MHD simulations.

astro-ph.SR↗

arXiv2026

The first selection of optimal targets for the Lazuli ExtraSolar Coronagraph's reflected starlight exoplanet campaign

Direct detection and characterization of exoplanet atmospheres via reflected starlight enables probes of composition of mature exoplanet systems (since flux is decoupled from age) at closer separations than via thermal emission (due to observations in visible wavelengths), and even probes of biosignatures. New and upcoming ground and space-based platforms are enabling these observations for the first time. Recently the 3-m Lazuli Space Telescope, from the Eric and Wendy Schmidt Observatory System, was announced, which will be equipped with the ExtraSolar Coronagraph (ESC) designed for reflected light gas giant exoplanet observations in visible wavelengths. In this paper we present 27 known giant exoplanets that make up the first target list for these observations. Our target list spans ages and temperatures of planets not probed before by direct imaging, but some of which will also be accessible to the \textsl{Nancy Grace Roman} Coronagraph instrument and the ELT-METIS thermal imager, providing comprehensive atmosphere characterization and synergies. We discuss the science enabled by ESC observations of our target list and enhancements provided by future astrometric and radial velocity measurements.

astro-ph.EP↗

arXiv2026

Science-driven design and performance evaluation of a Silicon Laue Component lens for hard X-ray polarimetry

Focusing hard X-rays above 80 keV is challenging for grazing-incidence optics. Laue lenses offer an alternative based on crystal diffraction. We assess a lens based on Silicon Laue Components (SiLCs), consisting of stacked silicon plates, for polarimetry of accreting stellar-mass black holes and strongly magnetized neutron stars in the 80-200 keV band. We derive effective-area requirements from the minimum detectable polarization at 99% confidence (MDP99) for an XL-Calibur-inspired polarimeter observing a 100-mCrab source for 100 ks. We evaluate an 18-m-focal-length lens combining Si(220) and Si(111) reflections, with a 63 arcsec diffraction bandpass and a 90% optical filling factor, using on-axis ray tracing of a perfectly aligned lens with uniform crystal curvatures. An MDP99 of 5% requires mean effective areas of approximately 260, 400, and 740 cm^2 in the 80-100, 100-130, and 130-200 keV bands, respectively. The lens spans radii of 356-1453 mm and continuously covers 80-200 keV. Its effective area reaches approximately 375 cm^2 near the low-energy boundary and falls to approximately 200 cm^2 at 180 keV. The focal-plane diameters enclosing 50% and 90% of the focused signal are 5.4 mm (62 arcsec) and 9.8 mm (112 arcsec). Mean effective areas of approximately 320, 250, and 220 cm^2 give reference MDP99 values of approximately 4.5%, 6.4%, and 9.5% in the three bands. The design reaches few-percent polarimetric sensitivity below 100 keV but does not maintain a 5% MDP99 across 80-200 keV under the adopted detector assumptions. It remains relevant for more strongly polarized hard components and provides a reference for end-to-end optimization of the optics, detector response, and background.

astro-ph.IM↗