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At least 1,423 records · Page 79Linked to original sources

arXiv2026

Making Generative AI Workable at Work: Accountability, Localization, and Representation in Kenyan and Nigerian Small Businesses

Generative AI is widely framed as democratizing skilled work by lowering the barrier to producing professional output. Drawing on 21 semi-structured interviews with 27 workers across 21 small and medium-sized businesses (SMBs) in Kenya and Nigeria (July to October 2023), we find that availability alone did not make participation workable. In participants' accounts, drafting was rarely the hard part. The consequential work was making AI-assisted drafts defensible enough to circulate to clients. We identify five intertwined practices through which participants made generative AI workable: using it as draft labour, steering outputs through iterative interaction, switching and comparing across tools, performing release work to make outputs accountable before circulation, and repairing context misfit when outputs failed local requirements. We make two central contributions. First, we specify release work as a distinct, boundary-facing form of accountability labour, evidenced through verification gates, traceability reconstruction when sources fail, and non-delegation boundaries that decide what can ship. Second, we show how localization and representational failures, which we call context misfit, operate in everyday work as a recurring breakdown-and-repair class, including failures of local language, institutional knowledge, and the credible representation of African people and places, and we document the repair repertoire they demanded. Reframing democratization as workable participation, we argue that reported gains were conditional on this accountability and repair labour, and we translate the findings into four design directions for workplace generative AI. We treat Kenya and Nigeria as two situated cases, not as a representative sample of SMBs or the Global South.

cs.HC↗

arXiv2026

Unitarity methods for Magnus amplitudes

Magnus amplitudes refer to the matrix elements of the logarithm of the $S$-matrix. Modern unitarity methods construct cut-constructible parts of scattering amplitudes without directly invoking Feynman diagrams, offering an efficient way of obtaining loop integrands for scattering amplitudes. A procedure is presented that adapts unitarity methods to the computation of Magnus amplitudes.

hep-th↗

arXiv2026

From Signals to Evidence: Interpretable Underwater Acoustic Target Sensing with Large Language Models - Part II: Passive Detection

Underwater acoustic passive detection is essential to maritime surveillance and intelligent ocean sensing. Existing detection systems typically collapse multichannel array observations into a binary decision or a scalar confidence score, offering limited insight into the physical evidence that supports or contradicts a target declaration. This limitation is acute at low signal-to-noise ratio (SNR), where isolated spectral peaks and transient beamforming responses are inherently ambiguous and physically verifiable targets are characterized by an ensemble of coupled reinforcing cues. Building on the unified from-signals-to-evidence framework introduced in the companion Part I, this paper instantiates the framework for passive target detection. A physics-aware multichannel acoustic encoder encodes the array observations into complementary acoustic evidence tokens through a direction-guided architecture that combines minimum variance distortionless response (MVDR) processing with time-frequency and fine-frequency representations. These tokens are combined with environmental priors and fed into a pretrained large language model (LLM) that evaluates target-present and noise-only hypotheses and generates a structured detection report detailing target presence, bearing, spectral-line locations, received SNR, range, and per-field confidence scores. A physics-parameterized data engine extended to realistic oceanic channels provides traceable supervision at both discrete-label and structured-description levels. Experiments under white-noise and mixed ocean-background conditions demonstrate reliable detection, accurate bearing estimation, and physically interpretable structured reports, validating the feasibility of evidence-grounded underwater acoustic detection.

cs.SD↗

arXiv2026

Explicit quasi-periodic solutions to the discrete derivative nonlinear Schrödinger equation

Quasi-periodic solutions to the discrete derivative nonlinear Schrödinger equation are investigated. A Lax pair of the discrete integrable equation is derived, by which we obtain a nonlinear integrable symplectic map. Resorting to this map, the Riemann theta function expressions for discrete potentials are calculated. Consequently, we get the explicit solutions for the discrete derivative nonlinear Schrödinger equation, whose quasi-periodic properties are illustrated through the cases of genus one and genus two.

nlin.SI↗

arXiv2026

Stability, Tidal Deformability, and Statistical Constraints of Anisotropic Compact Stars with a Generalized Polytropic Equation of State : Modelling Observation with Buchdahl Space-Time

An anisotropic compact star model is constructed within the framework of general relativity by combining the Buchdahl space-time with a generalized polytropic equation of state. The model parameters are constrained through Darmois-Israel junction conditions by matching interior geometry smoothly with exterior Schwarzschild space-time. The proposed framework is applied to thirty-eight observed compact star candidates spanning a broad range of masses and radii. Regular and physically acceptable distributions of the energy density, radial pressure, tangential pressure, enclosed mass, anisotropy and gravitational redshift are obtained throughout the stellar interior. Energy density and pressure components are found to decrease outward, while the enclosed mass and gravitational redshift are found to increase monotonically towards the stellar surface. The radial pressure is required to vanish at the boundary, thereby determining the estimated stellar radius. The anisotropy is found to provide an additional outward contribution that supports the stellar configurations against gravitational collapse. The physical acceptability of the model is examined through the Zeldovich condition, radial adiabatic index, causality conditions and Herrera cracking criterion. Hydrostatic equilibrium is achieved through the balance among gravitational, hydrostatic and anisotropic forces. The tidal response is also investigated, and an inverse relation between the estimated mass and dimensionless tidal deformability is obtained. The calculated configurations span $2.88\lesssim M_{\rm Est}/M_\odot\lesssim3.23$ and yield $22.55\lesssimΛ\lesssim68.38$. MCMC analysis provides statistically constrained compactness and tidal parameters. Overall, a physically viable description of anisotropic compact stars is obtained.

gr-qc↗

arXiv2026

A Physically-Motivated Stochastic Model of Tropical Cyclone Dynamic Wind Fields

Tropical cyclones (TCs) pose significant risk to infrastructure and human safety, and the frequency and intensity of large TCs are expected to increase in the future. Quantifying such risk to coastal and inland regions is challenging, largely due to the rare nature of TCs which limits historical data availability. We introduce a new stochastic model of TC wind fields conditional on a small number of physical variables commonly found in synthetic and observed storm track databases. The model consists of a physically-motivated mean trend and an expansion of the residual field into a wavelet frame. We demonstrate the approach on a dataset consisting of historical data for storms impacting the northwest Gulf of Mexico. The model adequately captures asymmetry and nonstationary space-time correlation structures of observed wind fields.

stat.AP↗

arXiv2026

Graph-Recurrent Learning of Emergent Collective Dynamics

Collective ordering in correlated electron systems emerges from microscopic dynamics, yet the evolution of observable textures need not admit a closed, memoryless description. We introduce a graph-recurrent neural network (GRNN) framework that learns such emergent dynamics directly from trajectories without specifying microscopic equations of motion. The framework integrates gated recurrent memory with graph message passing: local hidden states encode information beyond the instantaneous observables, while spatial coupling allows this information to propagate between sites. The observable and hidden fields evolve jointly through a shared local rule, providing a scalable representation of dynamics with spatial interactions and temporal memory. We demonstrate this framework on the emergent dynamics of charge-density-wave order in the semiclassical Holstein model, successfully reproducing temporal and spatial correlations and dynamical scaling during domain coarsening. By learning finite-time evolution directly, the GRNN offers a route to efficient, data-driven descriptions of collective dynamics with unresolved microscopic degrees of freedom.

cond-mat.str-el↗

arXiv2026

Automating Feature Flag Cleanup at Scale with Multi-Agent LLM Systems: An Industrial Experience at DoorDash

Feature flags are runtime configuration mechanisms used for gradual feature rollouts, experimentation, and kill switches. Once a feature is fully rolled out, the flag and its gated code become technical debt and can introduce operational risk through inadvertently enabling dormant code paths. At DoorDash, over 56,000 feature flags span 623 repositories, with 1,659 currently stale as of May 2026, with internal estimates of one to two hours per manual cleanup. Existing rule-based cleanup tools proved insufficient in DoorDash's codebases. Our contribution is an end-to-end automated LLM-based feature flag cleanup system that integrates querying live feature flag rollout data using MCP, Jira ticket processing, and end-to-end pull request creation. Our system successfully produced engineer-approved flag removal PRs for 45 (90%) of the 50 stale flags it was evaluated on, proving the system's operational viability at scale with each cleanup averaging only $4.79 and 13.8 minutes. We evaluated the system's quality and reliability boundary based on success rates, cost per cleanup and time metrics. Furthermore, we discuss our lessons learned when architecting this system, to provide practical guidance for developing similar multi-agent systems.

cs.SE↗

arXiv2026

Automating quantum circuit optimization with AI agents

Compiling quantum algorithms for real devices involves hard optimization problems that can decide whether an algorithm runs at scale. We present a general method for solving such problems with AI agents, with every result certified. It builds on autoresearch loops, in which agents iteratively improve programs against a fixed evaluator and which have recently improved quantum cryptanalysis circuits, and adds two elements: a frozen, exact verifier certifies every result, and this verifier, together with the challenge around it, is built by an agent that formulates the problem with a researcher and validates the verifier against a solved case, deliberately broken solutions and published results before any solution exists. The method applies to any optimization problem whose solutions can be verified exactly and efficiently. We apply it to three compilation problems in quantum simulation, namely fermion-to-qubit encoding, Hamiltonian layout and unitary synthesis. In each, the agents improved substantially on published or standard constructions, reducing the cost being optimized by up to 73% at the largest sizes tested and outperforming a state-of-the-art optimizer for fermion-to-qubit encodings. Every submission is a program accompanied by its agent's notes, so the ideas behind each improvement are recorded in human-readable form; later agents built on them, and we describe them here. These results open the way to automated, certified discovery across quantum circuit design and beyond.

quant-ph↗

arXiv2026

Endpoint weighted Fourier estimate with vanishing spherical trace

We establish an endpoint weighted Fourier estimate under a vanishing restriction condition on the unit sphere. Let $n \geq 2$ and $p_c = 2(n+1)/(n+3)$. We prove that every $f \in L^{p_c}(\mathbb{R}^n)$ with zero spherical Fourier restriction satisfies $$ \left\| \bigl|1-|ξ|^2\bigr|^{-1/2}\widehat{f}(ξ) \right\|_{L^2(\mathbb{R}^n)} \leq C_n \|f\|_{L^{p_c}(\mathbb{R}^n)}, $$ where the restriction condition is understood through the continuous Tomas--Stein restriction operator. This result gives a negative answer to Goldberg's counterexample-existence question and supplies a missing endpoint.

math.CA↗

arXiv2026

ClinGuard: a governance framework for reliable, transparent, and auditable clinical coordination

ClinGuard is a model-agnostic governance framework that treats clinical coordination as patient-scoped, policy-bound transactions rather than free text. An independent sentinel layer validates and governs agent-proposed actions, escalates when needed, and executes approved actions through limited-privilege connectors. Each transaction is linked to a tamper-evident audit record, while an outcomes monitor informs future autonomy decisions. Using MIMIC-IV-FHIR and skilled-nursing-facility placement, we distinguish retrospective demonstration from proposed prospective evaluation in this work.

cs.CY↗

arXiv2026

Muffin - A Shared Memory Buffer for Asynchronous AI Agents

The rise of Agentic AI systems has changed the landscape of artificial intelligence by enabling strong reasoning capabilities through specialized multi-agent interaction with structured self-reflection. However, in target-driven reasoning tasks, single- and multi-agent systems are often on par in terms of performance gains. A major challenge is that failures often arise from an ineffective exchange of critical information during inter-agent interaction and coordination. Different from existing multi-agent debate paradigms that rely on synchronous turn-taking and sharing information across different agents, we propose an inter-agent protocol based on shared Memory bUFFer INference - referred to as MUFFIN - in an asynchronous agentic framework. In the first step, an orchestrator routes queries based on their reasoning demands and delegates demanding ones to a multi-agent setting, where a shared memory buffer provides asynchronous exchange of intermediate findings while an agent's private reasoning remains local. Our experiments show that our framework Muffin outperforms established competitors, achieving the best accuracy in 17 out of 20 of our benchmark settings, and provides a better quality-cost trade-off than multi-agent debate: Muffin improves accuracy by 19.2 accuracy points while using 77.4% fewer tokens, without requiring full-trace sharing.

cs.AI↗

arXiv2026

CrisiLangs: A Multilingual Resource for Crisis Warning Generation

Communicating with the public during an emergency entails issuing warning messages promptly. In Europe, it often requires producing them in more than one language. Recent work has shown that language models can be trained to generate warning messages as a crisis unfolds, but the available data covers only English. To fill this gap, we present CrisiLangs, a multilingual extension of CrisiText (an English dataset of crisis events paired with warning messages written according to expert communication guidelines) to Italian, Bulgarian, German, and Greek. Expert native speakers translated the warning messages of 1,000 events drawn from 411 scenarios, for a total of 5,000 event-message pairs across five languages. We test the resource to fine-tune a multilingual model on all five languages jointly, with and without the emergency guidelines in the prompt, and compare it with a zero-shot baseline. The fine-tuned models outperform the baseline on ROUGE-L, BLEU and BERTScore in every language and configuration. In a human preference study, low decoding temperatures receive the most preferences in all five languages and in both configurations, while the effect of the guidelines varies by language.

cs.CL↗

arXiv2026

In-Browser Human-AI Co-Writing for Counterspeech: An Exploratory User Study of Agency, Perceived Response Quality, and Time

Writing counterspeech that challenges online hate speech can be cognitively and emotionally demanding, whereas fully automated generation may weaken users' sense of voice and responsibility. We present an in-browser AI-assisted co-writing tool that enables users to contribute their own ideas and offers three editable suggestions generated by a large language model using dynamically retrieved hate speech-counterspeech examples from the Multitarget CONAN dataset. We evaluated the tool in an exploratory within-subject study with ten participants, complemented by think-aloud observations and semi-structured interviews with half of the participants. Participants wrote responses to three self-selected hate speech instances without assistance and then repeated the task with the tool. Final responses were subsequently peer-rated for empathy, non-toxicity, relevance, specificity, and persuasiveness. Participants evaluated the interface positively, and qualitative accounts linked user input, selection among alternatives, and small edits to perceived agency and responsibility. Assisted responses received higher descriptive mean ratings across all five proxies, with the largest difference for empathy. Mean composition time increased from 2.52 to 3.88 minutes. Qualitative findings further revealed a tension between empathy-oriented outputs and users' desire to express legitimate anger or direct disagreement. We contribute exploratory design guidance for treating AI-assisted counterspeech as deliberative co-writing rather than automated text production.

cs.SI↗

arXiv2026

Sum-rank analogues of Reed--Muller codes over Abelian Galois extensions

Let $\mathbb L/\mathbb K$ be a finite Abelian Galois extension with a fixed decomposition $\mathrm{Gal}(\mathbb L/\mathbb K)=\langleσ_1\rangle\times\cdots\times\langleσ_m\rangle$. We show that the quotient of the multivariate skew polynomial ring $\mathbb L[x_1,\ldots,x_m;σ_1,\ldots,σ_m]$ by suitable central polynomials $H_1(x_1),\ldots,H_m(x_m)$ is isomorphic to a product of $t$ copies of $\mathrm{End}_{\mathbb K}(\mathbb L)$, so that one skew polynomial represents all blocks of a sum-rank metric space. The sum-rank weight of an element equals the rank of an associated Dickson matrix and of a generalized Moore matrix. Using this, we define sum-rank Reed--Muller-type codes and show that their dimension and minimum distance are given by the formulas for affine Cartesian codes with side lengths $\mathrm{deg}\,H_1,\ldots,\mathrm{deg}\,H_m$; the same holds for codes supported on lower sets. The family contains as special cases linearized Reed--Solomon codes, Gabidulin codes, rank-metric Reed--Muller codes, and affine Cartesian codes. Over finite fields, every linearized Reed--Muller code of Berardini and Caruso is, after a monomial change of variables, a monomial code in our framework. This gives the dimension of all these codes, and their exact minimum distance in every degree whenever the support is a lower set.

cs.IT↗

arXiv2026

Finite Systems in the Space of Projective Measurements

We investigate the dynamics of two-level systems using the quasidistribution $Q$ function projected onto the space of collective measurements. Particular attention is given to evolution under Hamiltonians that are invariant under particle permutations, for pure states that are either invariant or noninvariant under such permutations. This requires constructing an approximate analog of the star product between quasidistribution functions: the projected $Q$ function of a product of two operators is expressed approximately in terms of the individual operators' $Q$ and $P$ functions. We rewrite the Liouville equation to determine approximate state dynamics and compare the result with exact evolution using the Hellinger distance. Overall, the method performs well for permutation-invariant pure states and Hamiltonians, whereas deviations arise for arbitrary pure states and noninvariant Hamiltonians.

quant-ph↗

arXiv2026

Spectral Properties of X-Ray Flares in Gamma-Ray Bursts

We conducted a systematic spectral analysis of the X-ray flares of gamma-ray bursts (GRBs) observed by Swift/X-ray Telescope from 2005 April to 2015 March. The sample includes 521 flares from 206 GRBs. The GRBs cover the three subtypes: X-ray flashes (XRFs), X-ray-rich GRBs (XRRs), and classical GRBs (C-GRBs). We used a simple power-law model to fit the spectra during the flares, as well as to fit the preflare and postflare afterglow emissions, and obtained the parameters such as photon index and intrinsic hydrogen column density ($N_{\rm H}$). By comparing the spectral characteristics of flares, prompt emission, and afterglow emission, and analyzing the temporal evolution of the X-ray flare spectra, we obtained the following main conclusions. (1) The spectrum during the flare phase is significantly harder than that of the preflare and postflare phases, while there is no obvious difference in the spectrum between the preflare and postflare phases. (2) The intrinsic $N_{\rm H}$ shows no significant change between the preflare and flare phases, but significantly decreases after the flare. (3) The overall X-ray flare spectra are softer than those of the prompt emission. (4) For GRBs with multiple flares, the photon index softens with increasing flare peak time. (5) The X-ray flare spectra in XRFs are softer than those in C-GRBs. These results support the origin of X-ray flares being from the internal shock radiation driven by the late activity of the GRB central engine.

astro-ph.HE↗

arXiv2026

Gradient Discrete Schrodinger Bridge Matching for Peptide Generation

The optimal control of a discrete Schrödinger bridge is a discrete gradient, a difference of vertex potentials, but recent models such as MadSBM instead learn a free control on the edges by cross-entropy. We prove that this leaves the action uncontrolled: cross-entropy identifies the control only up to a state-dependent shift (a gauge), and the action is $\operatorname{KL}(r\|p)$ plus a gauge term the loss never constrains. We propose GDSBM, which writes the control as a difference of vertex potentials and adds the action to the loss; pinning the gauge costs no expressiveness and makes the action a property of the model, so the penalty acts on the model and not on the gauge. On peptide generation our MadSBM reimplementation has a free-gauge action of order $10^{8}$ for a gauge-fixed value of $6.5$, against $1.5$ for GDSBM, with no quality loss that three seeds can detect. The gradient form also fits guidance, which becomes the addition of a potential: guided toward a net charge of $+3$ at pH~7, GDSBM is closer than MadSBM, over three seeds, to its exact tempered target in total variation at every guidance strength (at $β=1$, $81\%$ of samples in the target window against $74\%$, for a target of $97\%$); we claim no statistical significance.

math.OC↗