arXiv preprints from January 1, 2026 through September 21, 2026 — 10:02:37 EST
Skills extend an LLM agent beyond its parametric knowledge, and the gain they promise rests on picking the right one. Deployed harnesses route by preloading every skill's metadata into the context, which disperses the agent's attention and caps the library size. Retrieval pipelines move the selection out of the context, but also out...
Room impulse responses can be estimated via the image source model (ISM) using the image source point cloud (ISPC) of a physical source. However, because the source movement changes the ISPC, estimating the ISPC at a new source position typically requires repeated acoustic measurements. We propose an optimal transport (OT) barycenter...
The $k$-server conjecture states that a deterministic online algorithm can achieve competitive ratio $k$ on every metric space. We prove the conjecture. Specifically, we show that the work function algorithm satisfies it. Our proof uses a natural algebraic representation of the work function as a matrix, which encodes all feasible...
When a video model generates physically incorrect motion, did it fail to learn the correct motion, or did it learn it but fail to use it? We show the latter: the correct motion remains available inside the model and can still be made to control the generated video. We train on videos where red masses oscillate slowly and blue masses...
Designing social media experiences that support well-being requires understanding when, how, and for whom use matters. Screen-time totals omit content and context, and connecting these with behavior and experience requires coordinating measurements across timescales. We introduce the CAST framework to connect measurement choices with...
The feedback capacity of power-constrained channels with additive stationary Gaussian noise was formulated by Kim in \cite{kim2010feedback} as an infinite-dimensional optimization over the spectrum of an independent stationary Gaussian component and a strictly causal feedback filter. Kim further asserted that the independent...
Mobile manipulators deployed across many rooms and visits should improve with experience: after discovering that a cabinet is locked or finding an object in a drawer, the robot should reuse that knowledge rather than start each task from scratch. Yet today's robots often treat each task as new: compact scene representations omit...
Transformer representations evolve through learned additive transformations that either preserve their current direction or redirect it. We study this evolution as a functional geometry, decomposing learned updates into parallel and perpendicular components. Across pretrained models, we find substantial parallel components beyond the...
Many quantum sensors infer parameters from continuously emitted fields, yet in the presence of unmonitored loss, it is difficult to determine how much critical enhancement survives in the accessible output. In this work, we calculate the full frequency-resolved output quantum Fisher information (QFI) of a stationary vacuum-seeded...
Foundation models have progressed from learning and reasoning over existing knowledge, to increasingly learning through action, tool use, and outcome feedback. We argue that the next frontier is a further transition: from solving and acting within problems specified by humans to participating in the process by which new problems,...
As language models become more capable, long-term collaboration in learning, reasoning, and decision-making calls for a deeper understanding of the people they serve. Yet training such human-aware language models faces a fundamental supervision gap because current datasets for LLM assistant training contain few if any well-informed...
Let $k$ be a field of characteristic $p$. We show that if $\mathcal{F}$ is a saturated fusion system over a finite $p$-group $P$, and $V$ is an $\mathcal{F}$-stable indecomposable capped endopermutation $kP$-module whose Dade class $[V]$ belongs to the subgroup $D_k^Ω(P)$ of the Dade group $D_k(P)$ generated by all the relative...
We investigate how neutrino-mass constraints from cosmology depend on the assumed thermal history of the universe. Photon injection after neutrino decoupling would decrease the neutrino abundance inferred from the temperature of the cosmic microwave background (CMB), potentially loosening the upper limit on their masses. We first...
We confront a class of dark-energy equations of state emerging from group field theory (GFT) quantum gravity with DESI Data Release 2 baryon acoustic oscillations and Pantheon+ type-Ia supernovae. We introduce sampling parametrisations that replace microscopic initial-condition parameters by combinations more directly measured by...
Laser-plasma accelerators have demonstrated the ability to produce ultrashort relativistic electron bunches with peak currents suitable for compact light sources, ultrafast diffraction, and strong-field studies. However, their performance critically depends on preserving the longitudinal phase-space density of the beam as it exits the...
The ultraviolet continuum from a high-redshift accreting black hole is routinely used to infer its mass. GN-z11 offers a sharp test of whether that inference is secure. A thin-disk fit to its continuum gives an Eddington mass of $M_{\rm E}=1.12\times10^{7}\,\Msun$, an order of magnitude above the $\log(\Mbh/\Msun)=6.2\pm0.3$ inferred...
In this work, we investigate the finite-bias charge current, shot noise, and Fano factor in an Aharonov-Bohm interferometer composed of two parallel quantum dots, each side-coupled to a topological superconducting nanowire represented by an effective Kitaev chain hosting Majorana bound states at its ends. The transport properties are...
Let $X$ be an irreducible reduced projective subvariety of a Chow variety. On a generic smooth parameter locus, normal motions of resolved components define a quadratic Wasserstein metric, even when the cycles are singular, reducible, or carry multiplicities. Its metric completion is canonically $X^ν$: resolution fibers collapse,...
Relative navigation is a fundamental task in space proximity operations and autonomous rendezvous. The nonlinear relative orbital dynamics are equivariant and admit a semidirect product Lie group symmetry. This property is used to design an Equivariant Filter (EqF). The filter estimates the relative position and velocity between a...
High-dimensional representations of pretrained speech foundation models have proven beneficial for objective speech quality and intelligibility prediction. While existing work on neural intelligibility prediction usually leverages such representations for task-specific fine-tuning, in this work we evaluate the usefulness of such...
This work enhances global path planning via a pure-pursuit controller with multi-model kinematic switching that sustains plan fidelity across diverse terrains. The system includes a traversability graph for terrain analysis, a Heading-Aware A* algorithm for generating feasible paths, and a multi-model Pure Pursuit controller for...
We introduce an adaptive multi-parameter variant of ADMM and prove that it exhibits local superlinear convergence once the set of active constraints has been identified. In simulations, the proposed algorithm consistently outperforms OSQP, a standard ADMM solver, in terms of iteration count. We then implement an MPC solver based on...
Control barrier function safety filters enforce constraints by modifying a nominal input, but the resulting switching can destabilise the closed loop even when the nominal and filtered modes are individually stable. This paper presents a design framework for safe and globally exponentially stabilising controllers for linear systems...
Robots are increasingly used in diverse application areas, where autonomous navigation plays a central role. As these systems become more widespread, improving their energy efficiency is critical to extending operational time and reducing environmental impact. The Robot Operating System (ROS) is a widely adopted middleware for...
Optimal dispatch of battery energy storage systems requires balancing energy arbitrage against cycle-induced degradation, which is accurately quantified through rainflow cycle counting. However, rainflow's combinatorial, nondifferentiable algorithm is incompatible with both convex optimization and gradient-based neural network...