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while there are a plethora of works showing the effectiveness of
llms
llm reasoning
while there are a plethora of works showing the effectiveness of llms in generating step-by-step solutions through chain-of-thought cot reasoning on reasoning benchmarks little is understood about whether the generated cot is helpful for end-users in improving their ability to comprehend mathematical reasoning problems and detect errors hallucinations in llm-generated solutions
reinforcement learning rl algorithms are designed to optimize problem-solving by
learning
reinforcement learning rl
reinforcement learning rl algorithms are designed to optimize problem-solving by learning actions that maximize rewards a task that becomes particularly challenging in random and nonstationary environments
empirical results on real-world dual graphs show convergence under target distributions unrelated to
spanning
spanning trees
empirical results on real-world dual graphs show convergence under target distributions unrelated to spanning trees
dataset bias where data points are skewed to certain concepts is ubiquitous in
machine
machine learning
dataset bias where data points are skewed to certain concepts is ubiquitous in machine learning datasets
next we consider the case of multiple sensors each with its own
wireless
wireless communication
next we consider the case of multiple sensors each with its own wireless transmitter queue and show that our analysis extends to the case of multiple homogeneous sensors
approximately optimal distributed controls for high-dimensional stochastic
systems
control systems
approximately optimal distributed controls for high-dimensional stochastic systems with pairwise interaction through controls
in the current work we provide theoretical results for testing in dependence between pairs of paths of most commonly studied non-stationary gaussian processes - standard brownian motion and fractional
brownian
brownian motion
in the current work we provide theoretical results for testing in dependence between pairs of paths of most commonly studied non-stationary gaussian processes - standard brownian motion and fractional brownian motion fbm
multimodal large language models mllms have advanced
vision-language
vision-language models
multimodal large language models mllms have advanced vision-language reasoning and are increasingly deployed in embodied agents
furthermore by using the meta-algorithm with the double greedy algorithm we obtain a 1 2 -approximation for unconstrained non-monotone
submodular
-approximation algorithm
furthermore by using the meta-algorithm with the double greedy algorithm we obtain a 1 2 -approximation for unconstrained non-monotone submodular maximization under noise
we propose a novel approach for dynamic negative prompting in
diffusion
diffusion models
we propose a novel approach for dynamic negative prompting in diffusion models that leverages vision-language models vlms to adaptively generate negative prompts during the denoising process
through the judge s eyes inferred thinking traces improve reliability of
llm
llm reasoning
through the judge s eyes inferred thinking traces improve reliability of llm raters
the goal of policy learning is to train a
policy
policy learning
the goal of policy learning is to train a policy function that recommends a treatment given covariates to maximize population welfare
this work presents a physics-informed machine learning framework for modeling and predicting sisr in the
stochastic
langevin dynamics
this work presents a physics-informed machine learning framework for modeling and predicting sisr in the stochastic fitzhugh-nagumo neuron
first we formally define multiclass local calibration and
establish
local calibration
first we formally define multiclass local calibration and establish its relationship with strong calibration
normative reasoning is a type of reasoning that involves
normative
reasoning capabilities
normative reasoning is a type of reasoning that involves normative or deontic modality such as obligation and permission
we design meta-algorithms that reduce the problem to the unweighted
approximate
pattern matching
we design meta-algorithms that reduce the problem to the unweighted approximate maximum cardinality matching mcm problem
discovering causal relationships using proxy variables under
unmeasured
causal effects
discovering causal relationships using proxy variables under unmeasured confounding
cave detecting and explaining commonsense anomalies in
visual
spatial reasoning
cave detecting and explaining commonsense anomalies in visual environments
a robust measurement instrument is essential to correctly evaluate
trust
trustworthy ai
a robust measurement instrument is essential to correctly evaluate trust from a human-centered perspective
the results demonstrate the power benefits of
resolution
brain decoding
the results demonstrate the power benefits of resolution reconfigurable front-ends and their wide applicability to neural decoding problems
to investigate the neural representations that
emerge
neural networks
to investigate the neural representations that emerge in these networks we develop an analytical framework that maps the optimization over the network weights into a mean-field problem over the distribution of neural preactivations
on quantile treatment effects rank similarity and variation of
instrumental
average treatment effect
on quantile treatment effects rank similarity and variation of instrumental variables
to formalize this we extend the concept of data informativity by requiring the existence of a controller that stabilizes all
systems
control systems
to formalize this we extend the concept of data informativity by requiring the existence of a controller that stabilizes all systems consistent with the data and the prior knowledge
the system is implemented in ros gazebo turtlebot3 and evaluated with pathbench metrics including success rate collision rate
path
motion planning
the system is implemented in ros gazebo turtlebot3 and evaluated with pathbench metrics including success rate collision rate path efficiency and re-planning efficiency in dynamic and partially observable environments
its time complexity is o m log l n log n where n is the size of the domain of mathcal i
m
query complexity
its time complexity is o m log l n log n where n is the size of the domain of mathcal i m is the number of nonzero instances of atomic roles in mathcal i and l is the number of distinct fuzzy values used in such instances plus 2
we develop a general framework for causal
effect
causal inference
we develop a general framework for causal effect estimation that relaxes the commonly assumed requirement that exposures contain higher-frequency variation than confounders
to mitigate this mixture-of-experts moe architectures activate only a small portion of parameters during
inference
moe inference
to mitigate this mixture-of-experts moe architectures activate only a small portion of parameters during inference significantly lowering both memory demand and computational overhead
researchers often use specifications that correctly estimate the
average
average treatment
researchers often use specifications that correctly estimate the average treatment effect under the assumption of constant effects
the simulated galaxies reproduce the observed stellar-to-halo mass mass--metallicity and size--mass relations yielding stellar masses of 10 6-10 8 m_ odot and metallicities consistent with those of
local
galactic disk
the simulated galaxies reproduce the observed stellar-to-halo mass mass--metallicity and size--mass relations yielding stellar masses of 10 6-10 8 m_ odot and metallicities consistent with those of local group dwarf galaxies
demonstrating broad applicability our system has been evaluated across diverse domains including operations research
machine
machine learning
demonstrating broad applicability our system has been evaluated across diverse domains including operations research machine learning gpu kernel optimization and classical mathematical problems
this work lays a foundation for future quantum
computing
quantum computing
this work lays a foundation for future quantum computing investigations of more complex and physically rich fermion-boson quantum field theories in higher dimensions
the model is trained on heterogeneous qps to minimize the
expected
policy learning
the model is trained on heterogeneous qps to minimize the expected objective value evaluated on the projected solutions
the framework provides practical tools for detecting when localized
treatments
average treatment
the framework provides practical tools for detecting when localized treatments become systemic and identifying critical thresholds for policy intervention
the latter is based on a fisher information matrix measuring the
sensitivity
surrogate brain
the latter is based on a fisher information matrix measuring the sensitivity of the neural activity to changes in the projection space
in this study we used a data-driven network approach to examine whether resting-state
eeg
human brain
in this study we used a data-driven network approach to examine whether resting-state eeg connectivity patterns differentiate individuals according to their creative abilities
in this work we present divrit a novel system for hebrew diacritization that frames the task as a zero-shot
classification
vision-language models
in this work we present divrit a novel system for hebrew diacritization that frames the task as a zero-shot classification problem
we introduce a general diploid population model with self-fertilization and possible overlapping generations and study the genealogy of a sample of n genes as the population
size
large population
we introduce a general diploid population model with self-fertilization and possible overlapping generations and study the genealogy of a sample of n genes as the population size n tends to infinity
the evolutionary mechanisms of cooperative behavior represent a fundamental topic in complex systems and
evolutionary
evolutionary game
the evolutionary mechanisms of cooperative behavior represent a fundamental topic in complex systems and evolutionary dynamics
task and motion planning tamp integrates high-level task
planning
obstacle avoidance
task and motion planning tamp integrates high-level task planning with low-level motion feasibility but existing methods are costly in long-horizon problems due to excessive motion sampling
control-var can estimate average treatment effects on the treated for dummy policies or
average
treatment effect
control-var can estimate average treatment effects on the treated for dummy policies or average causal responses over time for continuous policies
jacobian-based interpretation of nonlinear neural
encoding
encoding models
jacobian-based interpretation of nonlinear neural encoding model
tilde o n epsilon d update time and o 1 rounds we obtain o_
epsilon
-approximation algorithm
tilde o n epsilon d update time and o 1 rounds we obtain o_ epsilon log n -approximate dynamic and mpc algorithms for k -median and earth-mover distance in mathbb r d
we propose geometrically-regularized world models grwm which enforces that consecutive points along a natural sensory trajectory remain close in latent
representation
world models
we propose geometrically-regularized world models grwm which enforces that consecutive points along a natural sensory trajectory remain close in latent representation space
we formulate the problem as the minimization of the total transmit power subject to signal-to-interference-plus-noise
ratio
signal-to-noise ratio
we formulate the problem as the minimization of the total transmit power subject to signal-to-interference-plus-noise ratio sinr constraints for communication users and mean-squared-error mse constraints for radar sensing
modeling predator-prey dynamics with stochastic differential equations patterns of collective hunting and nonlinear
predation
ecological interactions
modeling predator-prey dynamics with stochastic differential equations patterns of collective hunting and nonlinear predation effects
our findings reveal asymmetric bidirectional causality with urban systems exerting stronger influences on
traffic
traffic dynamics
our findings reveal asymmetric bidirectional causality with urban systems exerting stronger influences on traffic dynamics than the reverse in most cities
many believe that intracortical axons conduct signals too slowly to bring the
contextual
surrogate brain
many believe that intracortical axons conduct signals too slowly to bring the contextual information from receptive fields of other neurons
we investigate a quantum heat engine where energy exchanges are driven by generalized
measurements
quantum coherence
we investigate a quantum heat engine where energy exchanges are driven by generalized measurements and the sequence of these operations is coherently controlled in a superposition of causal orders
distributed quantum computing dqc provides a promising route toward scalable quantum
computation
open quantum
distributed quantum computing dqc provides a promising route toward scalable quantum computation where entanglement-assisted locc and circuit knitting represent two complementary approaches
this paper focuses on the transceiver design of ofdm systems based on dms provides an illustration of the potential of dms in
wireless
wireless systems
this paper focuses on the transceiver design of ofdm systems based on dms provides an illustration of the potential of dms in wireless transceivers and points out the related research directions brought by dms
large language models llms excel at general tasks but underperform in specialized
domains
language agents
large language models llms excel at general tasks but underperform in specialized domains like economics and psychology which require deep principled understanding
building new end-effectors in simulation the
robot
mobile robots
building new end-effectors in simulation the robot can identify the general tool geometries most beneficial for a task
coherent perfect absorption zero reflection without
linewidth
optical interference
coherent perfect absorption zero reflection without linewidth suppression
a theory of the mechanics of information generalization through measurement of uncertainty
learning
policy learning
a theory of the mechanics of information generalization through measurement of uncertainty learning is measuring
they do not break network nonlocality which enables one to identify useful quantum
channels
network nonlocality
they do not break network nonlocality which enables one to identify useful quantum channels in networks
there are two prevalent ways to constructing 3d scenes procedural
generation
layout generation
there are two prevalent ways to constructing 3d scenes procedural generation and 2d lifting
in the absence of disturbances we find that standard
inverse
inverse optimal issf
in the absence of disturbances we find that standard inverse optimal safe controllers have a certain degree of gain margin
many protocols require single photons often approximated by strongly attenuated
laser
single photons
many protocols require single photons often approximated by strongly attenuated laser pulses
most counterfactual inference frameworks traditionally assume acyclic structural
causal
causal effects
most counterfactual inference frameworks traditionally assume acyclic structural causal models scms i
we formulate the problem as a markov decision process and analyze the structure of the optimal
policy
reinforcement learning
we formulate the problem as a markov decision process and analyze the structure of the optimal policy pi star for l 3 extending insights to arbitrary l
we further establish unconditional lower bounds demonstrating that the time and query complexities of our algorithms are optimal up to mathrm polylog n factors hidden within the
tilde
-time algorithm
we further establish unconditional lower bounds demonstrating that the time and query complexities of our algorithms are optimal up to mathrm polylog n factors hidden within the tilde o cdot notation below
these limitations are particularly apparent in real-life driving
scenarios
obstacle avoidance
these limitations are particularly apparent in real-life driving scenarios where state-of-the-art algorithms struggle to safely or reliably complete overtaking manoeuvres
based on the real-world hierarchical structure of resource allocation this paper presents a coupled dynamic model of resource allocation and epidemic spreading that incorporates a role-based division of network
nodes
network structures
based on the real-world hierarchical structure of resource allocation this paper presents a coupled dynamic model of resource allocation and epidemic spreading that incorporates a role-based division of network nodes into resource allocators and recipients
we resolve this question by presenting the first bidirectional reduction showing that suffix
array
suffix array
we resolve this question by presenting the first bidirectional reduction showing that suffix array queries are up to an additive o log log n term in query time equivalent to prefix-select queries in all parameters
considering structured data with an underlying feature space of small dimension we show that maximizing the
mutual
mutual information
considering structured data with an underlying feature space of small dimension we show that maximizing the mutual information implies i finding an appropriate projection space and ii building a neural representation with the appropriate metric
on the algorithmic side we prove that mso _2 -discovery is in xp when parameterized by treewidth and that
mso
tree embedding
on the algorithmic side we prove that mso _2 -discovery is in xp when parameterized by treewidth and that mso _1 -discovery is fixed-parameter tractable when parameterized by neighborhood diversity
these results have implications for amplified oversight -- the challenge of combining humans and ai to supervise ai
systems
ai assistance
these results have implications for amplified oversight -- the challenge of combining humans and ai to supervise ai systems even as they surpass human expert performance
gouy phase-related effects in the free-space optical modulation of
free
nonlinear optical
gouy phase-related effects in the free-space optical modulation of free electrons
the system achieves a high energy resolution of
approximately
measurements demonstrated
the system achieves a high energy resolution of approximately 11
across 7 datasets wimhf identifies a small number of human-interpretable features that account for the majority of the
preference
preference data
across 7 datasets wimhf identifies a small number of human-interpretable features that account for the majority of the preference prediction signal achieved by black-box models
grid cells in the medial entorhinal cortex provide a periodic spatial code that are organized near a
toroidal
grid cell
grid cells in the medial entorhinal cortex provide a periodic spatial code that are organized near a toroidal manifold independent of the spatial environment
maps converts attribution maps into explanation-masked images emis and compares image-by-image human accuracies on these minimal
images
visual stimuli
maps converts attribution maps into explanation-masked images emis and compares image-by-image human accuracies on these minimal images with limited pixel budgets with accuracies on the full stimuli
in this article we present the derivation of the theoretical foundations for cooperative task spaces of multi-arm
robotic
robotic systems
in this article we present the derivation of the theoretical foundations for cooperative task spaces of multi-arm robotic systems based on geometric primitives defined using conformal geometric algebra
nonetheless despite the potential of such tools for linguistic research comprehensive evaluation of their performance and impact on the creation of annotated datasets especially under a perspectivized approach to
nlp
natural language processing
nonetheless despite the potential of such tools for linguistic research comprehensive evaluation of their performance and impact on the creation of annotated datasets especially under a perspectivized approach to nlp is still missing
by representing the robot s physical topology as a graph the proposed gnn-based policy captures coupling among components enabling faster and more stable
learning
reinforcement learning
by representing the robot s physical topology as a graph the proposed gnn-based policy captures coupling among components enabling faster and more stable learning than conventional multilayer perceptron mlp policies
yet an important question still remains are video models ready to serve as zero-shot reasoners in challenging
visual
reasoning tasks
yet an important question still remains are video models ready to serve as zero-shot reasoners in challenging visual reasoning scenarios
lexicon and syntax making it difficult to isolate the neural substrates of cognitively
deeper
neural representations
lexicon and syntax making it difficult to isolate the neural substrates of cognitively deeper processes
our work lays the foundation for this new direction by establishing upper and lower bounds on space
complexity
polynomial time
our work lays the foundation for this new direction by establishing upper and lower bounds on space complexity of key variants of the problem
by converting each dataset into interpretable metadata we prompt an
llm
large language models llms
by converting each dataset into interpretable metadata we prompt an llm to recommend both model families and hyperparameters
ct reconstruction provides radiologists with images for diagnosis and treatment yet current
deep
deep network
ct reconstruction provides radiologists with images for diagnosis and treatment yet current deep learning methods are typically limited to specific anatomies and datasets hindering generalization ability to unseen anatomies and lesions
using stochastic differential equation techniques we establish a
functional
stochastic differential
using stochastic differential equation techniques we establish a functional law of large numbers for the scaled populations of sensitive cells resistant cells and the initial resistant clone
building the optical setup for investigating biological
questions
optical properties
building the optical setup for investigating biological questions comes with challenges
different from existing motion representations we aim to estimate an hr
motion
optical flow
different from existing motion representations we aim to estimate an hr motion trajectory with high-quality from a single motion-blurred image
we hope our findings could inspire more efforts on re-examining redllm unlocking its potential for developing powerful and
efficient
models llms
we hope our findings could inspire more efforts on re-examining redllm unlocking its potential for developing powerful and efficient llms
based on these results we derive nn matching from
riesz
riesz regression
based on these results we derive nn matching from riesz regression
while ai-assisted participants completed several
tasks
ai agents
while ai-assisted participants completed several tasks faster and more accurately no significant pre-post differences emerged in standardized measures of problem solving or verbal comprehension
these results provide a unified distributional framework across these two classical regimes cite avramleonenkosakhno2010-esaim biermebonamileon2011-ejp that makes the unusual behavior of the estimates under fixed-domain
asymptotics
asymptotic normality
these results provide a unified distributional framework across these two classical regimes cite avramleonenkosakhno2010-esaim biermebonamileon2011-ejp that makes the unusual behavior of the estimates under fixed-domain asymptotics intuitively obvious
the material platform and arrangement can be implemented for both
monitoring
optical communication
the material platform and arrangement can be implemented for both monitoring and activating the actuators which makes the proposed system an attractive candidate for closed-loop control of optical devices
learning to plan schedule with reinforcement-learned bimanual
robot
motion planning
learning to plan schedule with reinforcement-learned bimanual robot skills
from amateur to master infusing knowledge into
llms
llm agents
from amateur to master infusing knowledge into llms via automated curriculum learning
smbhs are evolved following the stellar mass growth of their host galaxies by assigning an accretion
rate
star formation rates
smbhs are evolved following the stellar mass growth of their host galaxies by assigning an accretion rate at each redshift from the empirical eddington ratio distributions and duty cycles
second we observe that using english as a
pivot
multilingual data
second we observe that using english as a pivot language i
circuits designed by textsc flowq-net achieve significant improvements yielding
circuits
peaked circuits
circuits designed by textsc flowq-net achieve significant improvements yielding circuits that are 10 times -30 times more compact in terms of parameters gates and depth compared to commonly used unitary baselines without compromising accuracy
this work advances the reliability of emcd as a quantitative tool for magnetic characterization at the nanoscale with unknown
magnetic
magnetic anisotropy
this work advances the reliability of emcd as a quantitative tool for magnetic characterization at the nanoscale with unknown magnetic structures
samples were subjected to varied thermal and vibrational conditions and their crystallization onset and morphological evolution were examined through optical microscopy scanning
electron
electron microscopy
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
through the judge s eyes inferred thinking traces improve reliability of
llm
llm raters
through the judge s eyes inferred thinking traces improve reliability of llm raters
never too rigid to reach adaptive virtual model control with llm- and lyapunov-based
reinforcement
control strategy
never too rigid to reach adaptive virtual model control with llm- and lyapunov-based reinforcement learning
neural activity forecasting is central to understanding neural systems and
enabling
recurrent neural
neural activity forecasting is central to understanding neural systems and enabling closed-loop control
here we establish a quantitative framework to characterize the operational lifetime of embezzling catalysts focusing on their role in entanglement
distillation
multipartite entanglement
here we establish a quantitative framework to characterize the operational lifetime of embezzling catalysts focusing on their role in entanglement distillation and extending the analysis to quantum teleportation
experimental results show that the proposed method achieves state-of-the-art
fusion
image fusion
experimental results show that the proposed method achieves state-of-the-art fusion performance in qualitative and quantitative evaluations across multiple datasets and significantly improves semantic segmentation metrics