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a linear convergence rate is also obtained under the strong
monotonicity
point convergence
a linear convergence rate is also obtained under the strong monotonicity assumption
interpreting llms as credit risk classifiers do their
feature
llm raters
interpreting llms as credit risk classifiers do their feature explanations align with classical ml
our framework unifies riesz regression for automatic
debiased
machine learning
our framework unifies riesz regression for automatic debiased machine learning covariate balancing targeted maximum likelihood estimation tmle and density-ratio estimation
to our knowledge this is the first paper to obtain explicit policies for
spatial
expansion planning
to our knowledge this is the first paper to obtain explicit policies for spatial resource extraction with nonlinear growth and a fortiori closed-form markov equilibria on general networks
magnetic phase transitions between ordered
phases
phase transition
magnetic phase transitions between ordered phases are often understood on the basis of semi-classical spin models
in this work we answer it positively by providing both computational and statistical
convergence
convergence rate
in this work we answer it positively by providing both computational and statistical convergence guarantees of sgd
consider the setting in which a researcher is interested in the causal effect of a treatment z on a duration
time
treatment effect boundaries
consider the setting in which a researcher is interested in the causal effect of a treatment z on a duration time t which is subject to right censoring
while low-altitude uncrewed aerial vehicles uavs employed with wireless
power
transmit power
while low-altitude uncrewed aerial vehicles uavs employed with wireless power transfer wpt capabilities offer a promising solution the line-of-sight channels that facilitate efficient energy delivery also expose sensitive operational data to adversaries
we also leverage foundation models to propose a new concept-consistency metric c 2 -score that can be
used
foundation models
we also leverage foundation models to propose a new concept-consistency metric c 2 -score that can be used to evaluate concept-based methods
interpreting visual observations and natural
language
multi-goal visual
interpreting visual observations and natural language instructions for complex task execution remains a key challenge in robotics and ai
we find evidence that the dispersion in the bulge mz r is influenced by both stellar accretion from satellites and migration from the disk such that at a fixed bulge mass bulges with higher fraction of accreted and migrated
stars
bulge stars
we find evidence that the dispersion in the bulge mz r is influenced by both stellar accretion from satellites and migration from the disk such that at a fixed bulge mass bulges with higher fraction of accreted and migrated stars tend to be less metal-rich
our results indicate that although llms generally adhere to valid reasoning patterns they exhibit notable inconsistencies in specific types of
normative
llm inference
our results indicate that although llms generally adhere to valid reasoning patterns they exhibit notable inconsistencies in specific types of normative reasoning and display cognitive biases similar to those observed in psychological studies of human reasoning
we propose a data-driven framework for efficiently solving quadratic
programming
dynamic programming
we propose a data-driven framework for efficiently solving quadratic programming qp problems by reducing the number of variables in high-dimensional qps using instance-specific projection
first when granular town-level case histories are unavailable network information especially
interactions
case histories
first when granular town-level case histories are unavailable network information especially interactions between macro incidence and a town s network position yields large out-of-sample gains predict-r2 rising from 0
simulation results show the superior performance of our approach in comparison to alternatives such as inverse
propensity
propensity score
simulation results show the superior performance of our approach in comparison to alternatives such as inverse propensity score estimators and double machine learning estimators in finite samples
semantic representations emerge in biologically
inspired
neural representations
semantic representations emerge in biologically inspired ensembles of cross-supervising neural networks
the literature provides a wide range of estimates for this parameter and j
frequently
instrumental variable
the literature provides a wide range of estimates for this parameter and j frequently rejects the null of valid instruments
large language models llms have demonstrated exceptional
capabilities
vision-language models
large language models llms have demonstrated exceptional capabilities across multiple domains by leveraging massive pre-training and curated fine-tuning data
popular piml approaches including neural operators physics-informed neural networks neural ordinary differential equations and
neural
reinforcement learning
popular piml approaches including neural operators physics-informed neural networks neural ordinary differential equations and neural discrete equilibria are typically fit to objectives that simultaneously include both data and physical constraints
the state-of-the-art algorithms have much worse depth of n 1 2 o 1 rozho v n haeupler martinsson stoc 23 and
m
online algorithm
the state-of-the-art algorithms have much worse depth of n 1 2 o 1 rozho v n haeupler martinsson stoc 23 and m 1 o 1 respectively
our findings identify magnetic tidal coupling as a novel strong-gravity effect and establish its importance for the resonant dynamics of
compact-object
tidal field
our findings identify magnetic tidal coupling as a novel strong-gravity effect and establish its importance for the resonant dynamics of compact-object binaries near smbhs
we study diffusion on multiplex networks with directed
interlayer
multiplex networks
we study diffusion on multiplex networks with directed interlayer couplings
we also find an emergent conservation law for both the 2 -dtc and df
phases
-dtc phases
we also find an emergent conservation law for both the 2 -dtc and df phases while dynamical conservation arises periodically for the 4 -dtc phases
the prohibitive cost of evaluating large language models
llms
large language
the prohibitive cost of evaluating large language models llms on comprehensive benchmarks necessitates the creation of small yet representative data subsets i
for example comparing two neural systems can shed light on the nature of emergent
computations
neural representations
for example comparing two neural systems can shed light on the nature of emergent computations in the brain and deep neural networks
as a third alternative we propose artificial
intelligence
ai assistance
as a third alternative we propose artificial intelligence ai enabled representatives trained on individual shareholder preferences to act as proxies and vote on their behalf
we find a relation between narrower degree distributions and longer loops in investigating the lengths of the shortest
loops
network structures
we find a relation between narrower degree distributions and longer loops in investigating the lengths of the shortest loops in various networks with continuously changing degree distributions including three typical types of realistic scale-free networks classical erd os-r enyi random graphs and regular networks
in the proposed system the target illumination signal is transmitted on the downlink radar sub-band whereas uplink users
transmit
transmit power
in the proposed system the target illumination signal is transmitted on the downlink radar sub-band whereas uplink users transmit on the uplink communication sub-band
however the uniquely strict requirements for high-fidelity qubit transmission complicate the extent to which classical solutions may apply to future quantum
networks
entanglement entropy
however the uniquely strict requirements for high-fidelity qubit transmission complicate the extent to which classical solutions may apply to future quantum networks particularly in terms of recognizing noise sources present in low-flux nonunitary channels
bayesian optimization bo has the potential to solve various combinatorial tasks ranging from
materials
generative ai
bayesian optimization bo has the potential to solve various combinatorial tasks ranging from materials science to neural architecture search
siraj diverse and efficient red-teaming for llm
agents
llm agents
siraj diverse and efficient red-teaming for llm agents via distilled structured reasoning
this unified framework naturally reproduces the observed evolution of luminosity the core mass
function
stellar mass function
this unified framework naturally reproduces the observed evolution of luminosity the core mass function the mass growth of the most massive protostars and the dense gas star formation law on clump scales establishing a coherent picture of accelerating star formation across scales
tidal disruption events with sph-exa resolving the
return
tidal disruption
tidal disruption events with sph-exa resolving the return of the stream
these results highlight the significant room for improving the mathematical
reasoning
reasoning capabilities
these results highlight the significant room for improving the mathematical reasoning in current llms
samples were subjected to varied thermal and vibrational conditions and their crystallization onset and morphological evolution were examined through optical microscopy scanning electron microscopy sem energy dispersive x-ray spectroscopy eds and atomic force
microscopy
x-ray diffraction
samples were subjected to varied thermal and vibrational conditions and their crystallization onset and morphological evolution were examined through optical microscopy scanning electron microscopy sem energy dispersive x-ray spectroscopy eds and atomic force microscopy afm
a major problem in the study of large language models is to understand their inherent
low-dimensional
large language
a major problem in the study of large language models is to understand their inherent low-dimensional structure
tuning magnetic anisotropy through chemical doping is a powerful strategy for designing functional materials with enhanced
magnetic
magnetic anisotropy
tuning magnetic anisotropy through chemical doping is a powerful strategy for designing functional materials with enhanced magnetic properties
pre-trained forecasting models strong zero-shot feature extractors for time
series
time series classification
pre-trained forecasting models strong zero-shot feature extractors for time series classification
the optimization problem aims to maximize the communication sum-rate which is critical for ensuring high-quality service to users while also maintaining accurate sensing performance for detection tasks and adhering to maximum
power
optimization problem
the optimization problem aims to maximize the communication sum-rate which is critical for ensuring high-quality service to users while also maintaining accurate sensing performance for detection tasks and adhering to maximum power constraints for efficient resource usage
to capture the long-term dependency and complex dynamics of
eeg
brain activity
to capture the long-term dependency and complex dynamics of eeg we propose a hybrid encoder combining a mamba-like linear attention channel encoder and a spatiotemporal dynamics model
we propose that these early bulge-disk galaxies represent progenitors of massive star-forming and
quiescent
massive stars
we propose that these early bulge-disk galaxies represent progenitors of massive star-forming and quiescent systems observed at lower redshifts
in the online metric matching problem n servers and n requests lie in a
metric
online algorithm
in the online metric matching problem n servers and n requests lie in a metric space
evolutionary game theory offers a general framework to study how behaviors evolve by
social
evolutionary game
evolutionary game theory offers a general framework to study how behaviors evolve by social learning in a population
modelling and evaluating travel information during
disruptions
traffic dynamics
modelling and evaluating travel information during disruptions an illustrative example from swedish railways
urban transportation networks are inherently vulnerable to
disruptions
human mobility
urban transportation networks are inherently vulnerable to disruptions that affect connectivity and passenger mobility
however its reasoning still required careful supervision
particularly
prior knowledge
however its reasoning still required careful supervision particularly to correct subtle mistakes
wavefront curvature and transverse atomic motion in time-resolved
atom
atom interferometry
wavefront curvature and transverse atomic motion in time-resolved atom interferometry impact and mitigation
achieving high-fidelity compact rgbd imaging presents a dual challenge conventional compact optics struggle with rgb sharpness across the entire depth-of-field while software-only monocular depth
estimation
depth estimation
achieving high-fidelity compact rgbd imaging presents a dual challenge conventional compact optics struggle with rgb sharpness across the entire depth-of-field while software-only monocular depth estimation mde is an ill-posed problem reliant on unreliable semantic priors
767 by feng niazadeh and saberi for unweighted
graphs
bipartite graphs
767 by feng niazadeh and saberi for unweighted graphs whose second batch consists of independently arriving nodes
we then show that this low-rank structure can be leveraged for generation -- in particular we can generate a response to a target prompt using a linear combination of the model s
outputs
generative models
we then show that this low-rank structure can be leveraged for generation -- in particular we can generate a response to a target prompt using a linear combination of the model s outputs on unrelated or even nonsensical prompts
attncache accelerating self-attention inference for llm
prefill
llm post-training
attncache accelerating self-attention inference for llm prefill via attention cache
group size effects and collective misalignment in llm
multi-agent
group size
group size effects and collective misalignment in llm multi-agent systems
to address this gap we present a humanoid
visual-tactile-action
tactile sensors
to address this gap we present a humanoid visual-tactile-action dataset designed for manipulating deformable soft objects
this approach yields significantly improved latent
representations
representation learning
this approach yields significantly improved latent representations that align closely with the true topology of the environment
it also leads to the first non-trivial strongly
polynomial
strongly polynomial
it also leads to the first non-trivial strongly polynomial dynamic algorithm for minimum mean cycle
according to current physics models if such a transition occurred in any location a region of true
vacuum
vacuum decay
according to current physics models if such a transition occurred in any location a region of true vacuum would propagate outward at near light speed destroying the accessible universe as we know it by deeply altering the effective physical laws
this provides frequentist interpretations to mutual information and new
computational
computationally efficient
this provides frequentist interpretations to mutual information and new computational strategies for approximating it
graph-theoretical mapping of resting-state
eeg
fmri data
graph-theoretical mapping of resting-state eeg reveals neural signatures of creativity
linear regression with isotropic inputs and single-source
training
real-world datasets
linear regression with isotropic inputs and single-source training limiting their relevance to realistic settings
to address these issues this study introduces the
human-ai
human-ai interaction
to address these issues this study introduces the human-ai re synergy model hare-sm a conceptual framework that integrates ai-driven analysis with human oversight to improve requirements elicitation analysis and validation
chromium thin films deposited on silicon substrates by dc magnetron sputtering were systematically investigated as a function of film
thickness
film thickness
chromium thin films deposited on silicon substrates by dc magnetron sputtering were systematically investigated as a function of film thickness using a dc power of 50 w and a post-deposition annealing temperature of 200 c
while many real scale-free networks are known to contain shorter loops such as triangles it remains to investigate the distributions of longer
loops
complex networks
while many real scale-free networks are known to contain shorter loops such as triangles it remains to investigate the distributions of longer loops in more wide class of networks
to this end we introduce a novel metric for comparing both intrinsic
recurrent
recurrent neural networks
to this end we introduce a novel metric for comparing both intrinsic recurrent and input-driven dynamics called inputdsa idsa
it can provide guidance to efficiently shape the free-electron wave function for a wide range of
quantum
quantum dot
it can provide guidance to efficiently shape the free-electron wave function for a wide range of quantum applications
5 msun regime vary with environment defined as distance from the large-scale structure lss traced by nodes and filaments in the
cosmic
dark matter
5 msun regime vary with environment defined as distance from the large-scale structure lss traced by nodes and filaments in the cosmic web
specifically braincognizer introduces two modules the cognitive integration module which incorporates prior human knowledge to extract hierarchical region semantics and the
cognitive
brain-computer interface
specifically braincognizer introduces two modules the cognitive integration module which incorporates prior human knowledge to extract hierarchical region semantics and the cognitive correlation module which captures contextual semantic relationships across regions
our findings suggest that ultrafast spin transport or dipolar interactions or a combination of both may
play
phase transition
our findings suggest that ultrafast spin transport or dipolar interactions or a combination of both may play essential roles in the switching process
to evaluate tool-use rms we also introduce trbench _ bfcl a
benchmark
evaluation metrics
to evaluate tool-use rms we also introduce trbench _ bfcl a benchmark built on the agentic evaluation suite bfcl
cosmological hydrodynamical simulations are essential tools for studying the formation and evolution of galaxies and their central
supermassive
black hole
cosmological hydrodynamical simulations are essential tools for studying the formation and evolution of galaxies and their central supermassive black holes
within this framework we introduce the novel class-contextualized mixed
prompt
prompt tuning
within this framework we introduce the novel class-contextualized mixed prompt ccmp - based on class-specific prompts maintained alongside a globally shared prompt
furthermore we develop a deterministic asynchronous algorithm that exactly identifies the optimal set of nodes through
asynchronous
randomized algorithm
furthermore we develop a deterministic asynchronous algorithm that exactly identifies the optimal set of nodes through asynchronous update operations and progressive refinement ensuring both efficiency and precision
from unweighted to weighted dynamic matching in non-bipartite
graphs
bipartite graphs
from unweighted to weighted dynamic matching in non-bipartite graphs a low-loss reduction
2024 develops riesz regression for automatic debiased machine learning which directly estimates the
riesz
riesz regression
2024 develops riesz regression for automatic debiased machine learning which directly estimates the riesz representer or equivalently the bias-correction term by minimizing the mean squared error
for example convolutional neural networks are able to achieve remarkable
image
image classification
for example convolutional neural networks are able to achieve remarkable image classification accuracy across a wide range of applications in industry defense and other areas
the prior network generates coarse localization maps that delineate the approximate roi serving as spatial guidance for the dual
encoder
dual encoder
the prior network generates coarse localization maps that delineate the approximate roi serving as spatial guidance for the dual encoder network
unifying regression-based and design-based causal inference in
time-series
time-series experiments
unifying regression-based and design-based causal inference in time-series experiments
our findings provide actionable insights into how values are learned during post-training and help to inform data curation as well as the selection of models and algorithms for
preference
preference data
our findings provide actionable insights into how values are learned during post-training and help to inform data curation as well as the selection of models and algorithms for preference optimization to improve model alignment to human values
through systematic dynamical systems analysis we reveal that rl spontaneously discovers hybrid
attractor
dynamical systems
through systematic dynamical systems analysis we reveal that rl spontaneously discovers hybrid attractor architectures combining stable fixed-point attractors for decision maintenance with quasi-periodic attractors for flexible evidence integration
r3 adaptively samples training experience from diverse intersections with
environment
reinforcement learning
r3 adaptively samples training experience from diverse intersections with environment feedback-based priority and fine-tunes llm agents with a designed reward-weighted likelihood loss guiding reg-tsc toward high-reward policies across heterogeneous intersections
a major problem in the study of large language models is to understand their inherent
low-dimensional
language models
a major problem in the study of large language models is to understand their inherent low-dimensional structure
we revealed the fm-transition mechanism by showing that the difference in the local environment of each site is governed by cooperative evolution of spin correlations upon cooling giving rise to the
critical
phase transition
we revealed the fm-transition mechanism by showing that the difference in the local environment of each site is governed by cooperative evolution of spin correlations upon cooling giving rise to the critical phenomena
we prove certain monotonicity properties of the optimal policy in the
state
state estimation
we prove certain monotonicity properties of the optimal policy in the state space mathcal s and identify classes of unreachable states
enhancing the reachability of variational quantum
algorithms
open quantum
enhancing the reachability of variational quantum algorithms via input-state design
these findings underline the importance of thoughtful avatar design in healthcare applications to enhance user
experience
user experience
these findings underline the importance of thoughtful avatar design in healthcare applications to enhance user experience and engagement
to address these challenges we propose unifiedfl a dynamic
federated
federated learning
to address these challenges we propose unifiedfl a dynamic federated learning framework that represents heterogeneous local networks as nodes and edges in a directed model graph optimized by a shared graph neural network gnn
our results suggest that multi-agent debate when coupled with physics-grounded feedback is a promising new paradigm for automated
robot
robotic systems
our results suggest that multi-agent debate when coupled with physics-grounded feedback is a promising new paradigm for automated robot design
however for arbitrary qudit-based hardware platform the issue is that a generic
qudit
qubit readout
however for arbitrary qudit-based hardware platform the issue is that a generic qudit operation has to be decomposed into the sequence of native operations - pulses that are adjusted to the transitions between two levels in a qudit
this paper aims to address these challenges by revisiting and modifying classical
rl
reinforcement learning rl
this paper aims to address these challenges by revisiting and modifying classical rl approaches to efficiently operate in sparse randomized and nonstationary environments
to ensure computational tractability we introduce tight continuously differentiable reformulations of both the non-convex distance
constraints
soft constraints
to ensure computational tractability we introduce tight continuously differentiable reformulations of both the non-convex distance constraints and the chance constraints
to close this gap we introduce nano-scale
vision-language
vision-language models
to close this gap we introduce nano-scale vision-language action nanovla a family of lightweight vla architectures that achieve high performance with minimal resources
risk-aware safety filters with poisson safety functions and laplace
guidance
safety filter
risk-aware safety filters with poisson safety functions and laplace guidance fields
in this paper we situate context engineering provide a
systematic
context engineering
in this paper we situate context engineering provide a systematic definition outline its historical and conceptual landscape and examine key design considerations for practice
through comprehensive experimentation across six
diverse
llm inference
through comprehensive experimentation across six diverse tasks and utilizing six distinct llms our methodology demonstrates remarkable results achieving speeds up to 5
we focus on an important safety problem that is already challenging for humans fact-verification of
ai
reasoning capabilities
we focus on an important safety problem that is already challenging for humans fact-verification of ai outputs
collective systems that self-organise to maximise the group s ability to collect and distribute
information
collective systems
collective systems that self-organise to maximise the group s ability to collect and distribute information can be successful in environments with high spatial and temporal variation
recently proposed generative models for discrete data such as masked
diffusion
diffusion models
recently proposed generative models for discrete data such as masked diffusion models mdms exploit conditional independence approximations to reduce the computational cost of popular auto-regressive models arms at the price of some bias in the sampling distribution
land use change perceptions included deforestation
coastal
land use
land use change perceptions included deforestation coastal degradation habitat protection renewable energy facilities wetlands and others
we consider the task of ranked enumeration under min max orders as well as tasks concerning cqs with predicates of the form x min x where x is a set of variables and
x
query complexity
we consider the task of ranked enumeration under min max orders as well as tasks concerning cqs with predicates of the form x min x where x is a set of variables and x is a single variable counting enumeration direct access and predicate elimination i
turbulence impacted wavefront corrections using
beam
beam shaping
turbulence impacted wavefront corrections using beam modulation technique
traditional methods such as questionnaire assessments require manual intervention and webcam-based monitoring fails to provide accurate insights about learners mental focus as it is deceived by mere screen fixation without
cognitive
cognitive science
traditional methods such as questionnaire assessments require manual intervention and webcam-based monitoring fails to provide accurate insights about learners mental focus as it is deceived by mere screen fixation without cognitive engagement