Claims Browser

Generating action plans in natural language limits the plans to a high level and introduces ambiguity in action execution

"generating action plans in natural language tends to limit the generated plans to a high level, introducing ambiguity in action execution"

Most existing neural operator architectures enforce boundary conditions indirectly through training from data even though the boundary condition is often known exactly

"most existing neural operator architectures enforce boundary conditions indirectly through training from data even though the boundary condition is often known exactly"

Existing modifications that enforce boundary conditions explicitly suffer from impractical restrictions including boundary smoothness, uniform grids, and separable box-like domains

"existing modifications and approaches that do enforce boundary conditions explicitly suffer from impractical restrictions, including boundary smoothness, uniform grids, and separable, box-like domains"

Latent transitions in existing WAMs are commonly realized with Transformer-based predictors whose inductive structure is centered on token interaction rather than temporal evolution

"Yet latent transitions are commonly realized with Transformer-based predictors whose inductive structure is centered on token interaction rather than temporal evolution."

Existing work in agent domains uses domain-centered organization and heterogeneous evaluation that obscure common generation mechanisms and conflate candidate construction with verification and selection

"Existing work spans many agent domains, but domain-centered organization and heterogeneous evaluation often obscure common generation mechanisms and conflate candidate construction with verification and selection."

Research into event-based object classification methods is hindered by the lack of high-quality vision datasets

"Research into event-based object classification methods are hindered by the lack of high-quality vision datasets to use."

Conventional frame-based computer vision approaches have practical flaws including size, weight, power consumption constraints, security concerns with cloud computation, transmission latency, and connectivity requirements

"This approach has several practical flaws. The size, weight and power consumption of the device could prohibit deployment at the extreme edge or in covert sensing environments. Besides this, there are security concerns inherent in cloud-based or other off-device computation approaches due to the requirement of sending and receiving potentially sensitive data. Furthermore, this transmission of data introduces latency and requires consistent connectivity to the cloud infrastructure to function."

Existing reinforcement-learning methods for robot crowd navigation are limited because they output a single reactive action at each timestep, which constrains their ability to represent diverse short-term avoidance strategies

"Existing reinforcement-learning methods typically output a single reactive action at each timestep, which limits their ability to represent diverse short-term avoidance strategies."

Common crowd-navigation benchmarks have an evaluation artifact where learned agents can leave the valid domain and bypass dense crowds without explicit boundary constraints

"we observe an evaluation artifact in common crowd-navigation benchmarks: without explicit boundary constraints, learned agents may leave the valid domain and bypass dense crowds"

Existing output-stage uncertainty metrics can fail when models are overconfident on false assertions

"Existing output-stage uncertainty metrics can fail when models are overconfident on false assertions"

Existing benchmarks for ancient Chinese text recognition suffer from fragmentation in temporal coverage, medium diversity, and script type completeness

"However, existing benchmarks suffer from ''fragmentation'', manifested in limited temporal coverage, limited medium diversity, and incomplete script types."

Existing evaluations of spoken dialogue understanding often allow shortcuts based on transcripts or single-modality solutions, obscuring whether models genuinely ground predictions in speech

"existing evaluations often allow shortcuts based on transcripts or single-modality solutions, obscuring whether models genuinely ground predictions in speech"

LLM agent performance on anomaly detection and root-cause analysis tasks has not been systematically evaluated under controlled conditions

"their performance on these tasks has not been systematically evaluated under controlled conditions"

Current use-case benchmarks are insufficient because they only measure whether one agent completes one task, but fail to capture how changing capabilities, models, runtime mechanisms, capacity, and enterprise data should be managed organizationally.

"Use-case benchmarks show whether one agent completes one task, but not how changing capabilities, models, runtime mechanisms, capacity, and enterprise data should be owned, changed, admitted, or evidenced together."

Current LLM-based ontology learning systems fail to extract non-taxonomic relations due to limitations of closed, taxonomy-oriented relation vocabularies

"However, no non-taxonomic relations are extracted, highlighting limitations of closed, taxonomy-oriented relation vocabularies"

LLM outputs are treated as authoritative even when they are ungrounded or incorrect

"fluent, confident outputs are treated as authoritative even when ungrounded or incorrect"

Current LLM accountability frameworks suffer from under-specification of human oversight

"Four persistent gaps emerge: under-specification of human oversight, absence of shared accountability metrics, disciplinary disconnection, and limited empirical evaluation"

There is an absence of shared accountability metrics for LLMs across the field

"absence of shared accountability metrics"

Existing graph anomaly detection approaches use static basic functions for graph filters which cannot effectively adapt to frequency-domain distribution of graph data

"they use static basic function to constructed graph filter which cannot effectively adapt to the frequency-domain distribution of graph data"