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Showing 1301-1320 of 2685 claims of type "fact"
OCSRGlyph achieves state-of-the-art performance in optical chemical structure recognition by carefully considering stereochemistry.
MarkushGlyph improves upon prior Markush structure parsing systems by reading the entire structure as an image with a vision-language model, rather than using multiple stages to separately process visual and text input.
Graph neural network magnetic force-fields can learn effective magnetic energy functionals governing itinerant spin dynamics directly from electronic calculations, analogous to machine-learned interatomic potentials.
The GNN framework enables efficient evaluation of spin torques while capturing nonlinear and spatially extended interactions, accurately reproducing electronically generated spin torques.
This is the first systematic study of large language models for parent-order execution in algorithmic trading
In sparse mixture-of-experts language models, expert subspaces overlap substantially despite the expectation that co-selected experts should contribute distinct representation directions
Actual routing decisions in MoE models explain token representations better than matched alternative routes, even though expert subspaces overlap
FICE is the first learned cardinality estimator for BGP queries over knowledge graphs that generalizes to entirely unseen graphs without any retraining
BGP cardinality is a local function of the 2-hop neighborhood around bound terms in a factor-graph view of knowledge graphs
There exists a miscalibration threshold past which confidence-ranked auditing of LLM agents is worse than random auditing
Five open-weight LLMs show operationally useless near-constant confidence scores for self-auditing, while a proprietary model shows informative confidence
In agent auditing scenarios, the miscalibration threshold rises as the audit budget shrinks, contrary to a-priori expectations
PathVU enables programmatic scoring of region localization, visual recognition, quantity estimation, spatial reasoning, and insufficient-context judgment in pathology images
LLM agents acting as autonomous merchants fabricate product attributes in a majority of listings across models, even under instructions to be honest
CARP mechanism can suppress sales volume of dishonest LLM agents without requiring product-level ground truth, using only noisy complaint signals
Perception-Correction Distillation identifies correctable perception failures using downstream failure and teacher-student disagreement as complementary witnesses
Token importance in MLLMs evolves dynamically across layers rather than remaining fixed, requiring temporal trajectory modeling instead of snapshot decisions
Trend-aware Pruning can selectively reactivate late-blooming tokens that are initially undervalued but exhibit rising semantic importance
Some LLMs (Qwen3-32B and GPT-5-Mini) can match human post-stance distributions, but only when given participants' actual initial stances
All six tested LLMs fail to simulate initial human stances themselves and to produce faithful belief updates from self-generated stances
Pipeline data may be stale or degraded.
Last synthesis: 2026-09-20. 8,986 pending.