Tag: #research297 results
Proposes SVDD-JAD strategy mimicking police swerving to suppress stop-and-go waves via slow-in/fast-out maneuvers. Analyzes five key parameters measurable with roadside detectors. SUMO simulations confirm no secondary waves triggered.
PiT-PO uses reinforcement learning to evolve LLMs for symbolic regression, enforcing physical validity and parsimony. It treats LLMs as adaptive generators updated by search feedback. Achieves SOTA on benchmarks and discovers novel turbulence models.
Dual-stream unsupervised model fuses anomaly detection with physics proxies for embedded vehicle monitoring. Distinguishes transient shocks from sustained high-load states like hill climbing. Runs efficiently on RISC-V ECUs.
Presents Physics-Enhanced Swin Transformer (PEST) for 3D turbulence. Uses windowed attention, frequency loss, and physics constraints. Outperforms baselines in accuracy and stability.
PELLI is an iterative framework for integrating LLMs into software generation, evaluating code on maintainability, performance, and reliability. It tests five popular LLMs across three domains using Python standards. GPT-4T and Gemini outperform others, with prompt design impacting quality.
OmniVL-Guard unifies vision-language forgery detection and grounding with balanced RL. Self-Evolving CoT and ARSPO tackle difficulty bias. Excels in zero-shot generalization.
OmniSapiens-7B 2.0 uses HARPO RL to train a unified model across heterogeneous social tasks. HARPO balances learning via modulated advantages. It outperforms baselines by up to 16.85% with robust reasoning.
NSAM learns symbolic models and action masks automatically during DRL to avoid infeasible actions. It integrates symbolic reasoning with deep policy optimization mutually. Evaluations show improved sample efficiency and fewer violations.
White paper integrates nonuniform ellipticity breakthrough with topos-theoretic LRMs. Formalizes sharp Schauder estimates using ghost equations. Enables autonomous proofs in calculus of variations.
Neural Additive Experts use mixture-of-experts per feature with context-gated integration for flexible additivity. Targeted regularization ensures smooth transitions from additive to interactive models. Outperforms on accuracy while preserving feature explanations.