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LLMs are increasingly used to brainstorm research ideas, but existing evaluations mostly judge individual ideas by novelty, feasibility, or expert preference. We instead ask: how far are current LLM generated ideas from human researchers? To characterize this gap, we build a large scale evaluation framework for ideation from high quality human research papers. For each paper, we reverse engineer a small set of clo...
I revisit Type IIB axion dilaton Euclidean saddles in a specified axion charge sector. In that sector, the solution with $E=0$ is the BPS instanton, while $E 0$ gives non BPS wormholes with a smooth throat. The two cases solve the same radial equations but define different fluctuation problems. For the $E=0$ instanton, the Hamiltonian constraint, gauge quotient, charge sector boundary condition, and removal of col...
Nuclear fusion offers the promise of a clean, safe, and virtually limitless energy source, although it demands the development of advanced materials capable of withstanding extreme thermal, mechanical, and irradiation environments. In this regard, carbon based materials have emerged as critical candidates for both plasma facing and structural applications in fusion reactors. Among these, carbon fiber reinforced ca...
Electroencephalography (EEG) signals provide a unique opportunity for personal identification due to their inherent permanence and uniqueness. However, EEG data are complex, multidimensional, non stationary, and time dependent, making feature extraction and classification particularly challenging. This study proposes a framework by integrating autoencoder based feature extraction with self attention mechanism for ...
Introduction Auditory event related potential (ERP) brain computer interfaces (BCIs) offer communication support for individuals with amyotrophic lateral sclerosis (ALS) who eventually progress to completely locked in states. However, individual specific BCI pipeline optimization is technically demanding and time consuming, leaving substantial room for performance improvement in practice. A central challenge is in...
Flat bands have been theoretically predicted for decades but have only recently been realized in quantum materials such as magic angle twisted bilayer graphene, kagome and Lieb lattices, and rare earth metal compounds. To date, only twisted layered materials have enabled tuning of flat band energies near the electronic chemical potential, thereby influencing transport and thermodynamic properties. Here, we report ...
This paper investigates two central themes of contemporary transhumanism—human enhancement and artificial intelligence—from the perspective of Classical Islamic Philosophical Ethics (CIPE). First, it reconstructs the metaphysical framework and ethical orientation of CIPE through an analysis of its major representatives. Second, it examines the concept of enhancement in transhumanist thought in light of the metaphy...
We present World from Motion, a method for generating freely renderable dynamic 3D Gaussian representations from monocular videos. Our approach conditions a video model on dense, pixel aligned renderings that encode appearance, geometry, and 3D scene motion along both input and target camera trajectories to correct rendering artifacts and fill in missing regions from an initial reconstruction. To train this model,...
Prior work on imitation learning from suboptimal demonstrations typically relies on compressed supervision signals such as confidence estimates, discriminator scores, or importance weights. These scalar signals are inherently limited, as they cannot explicitly express intermediate reasoning about task progress, failure modes, or corrective actions. We propose a language critique framework for imitation learning fr...
Extracting the full power of non local correlations in prepare and measure (PM) scenarios requires precise control over the timing and structure of the receiver's measurements. Indeed, recent developments in entanglement assisted classical communication scenarios have shown that adaptive strategies where the receiver uses the transmitted message to guide their measurement choice can outperform standard non adaptiv...
Reinforcement learning (RL) has become a central component of post training large language models (LLMs), yet little is understood about how RL adaptation is distributed across transformer layers. Existing approaches typically update all model parameters uniformly, implicitly assuming that every layer contributes similarly to the gains obtained during RL post training. In this work, we challenge this assumption th...
Current brain computer interface (BCI) systems assume that neural signals can be decoded through relatively stable mappings between electrophysiological activity and computational outputs. However, empirical evidence in computational neuroscience and neural signal processing indicates that brain activity exhibits strong non stationarity driven by plasticity, contextual modulation, and temporal drift. This work int...
Background Characterized by high incidence rate, high disability rate, high mortality rate and high recurrence rate, stroke has become the second leading cause of death globally and the primary cause of adult disability. Though traditional rehabilitation methods have played a significant role in post stroke functional recovery, their therapeutic efficacy is limited. In recent years, brain computer interfaces (BCI)...
In a former paper a quantum induced warp drive was introduced by assuming a model ofmicroscopic cylinders as spacelike dimensions whereby the timelike dimension remains at itsclassical one dimensional state. Described now is more detailed the underlying fundamentalphysical and mathematical spacetime structure than in the first paper , which is announced tofeature this quantum induced warp drive concept. Although t...
Quantum computing has emerged as a promising computational paradigm for machine learning (ML), with the potential to offer computational advantages over classical approaches. At this stage, the evidence supporting the performance and advantages of quantum machine learning (QML) models relative to classical models is insufficient.To address this gap, this paper presents an empirical study on the performance of QML ...
Sample based generative models are increasingly used for probabilistic forecasting in high stakes decision settings, yet their training objectives are blind to the decision maker's cost structure. These models are commonly trained with strictly proper scoring rules, such as the energy score, which allocate their training signal in proportion to data density, with no awareness of where forecast errors are most cost...
Fast quadrotor flight requires safe obstacle avoidance under tight onboard compute limits. While 3D Gaussian Splatting (3DGS) provides a continuous, geometry aware scene representation for perception driven navigation, existing 3DGS safety filters use reduced order models such as single and double integrators that ignore actuator limits and assume commanded accelerations are realized instantaneously. Building on a...
In autonomous laboratories, AI agents suggest the next batch of experiments to do. However, planning and executing those tasks taking full advantage of the available resources is a completely different question. This can be challenging when dealing with real world hardware constraints, especially so when there are multiple instruments with different capacities and throughputs. Here we demonstrate a 2 step method t...
Symmetry is a powerful inductive bias, but in quantum reservoir computing (QRC) it cannot be imposed only by making the reservoir symmetric. QRC maps inputs through fixed quantum dynamics into nonlinear expectation value features and trains only a classical readout, so the relevant symmetry must be visible in the measured feature map. We study cyclic forecasting tasks, such as sensors around a turbine or weather s...
Combinatorial optimization (CO) problems are difficult because certifiable discrete structure induces exponential search. One needs to search over the set exponentially many candidates to certify optimality, however, the structural feasibility of a path, packing, or cover can be verified in polynomial time once supplied. In this study, we introduce Neural Certificate Pricing (NCP) that exploits this asymmetry unde...
