ARIA addresses contextual tunneling in LLMs by conditioning knowledge use on mechanistic completeness. It uses a three-tier cascade for causal reasoning, physics-informed transfer, and parametric fallback, and improves materials discovery through auditable, physically grounded reasoning.
HyperAdapter introduces a hypergraph-based adapter that performs structured, group-aware adaptation in vision transformers by operating in hyperedge space rather than token space. It uses prototype-based assignments to build a soft hypergraph, aggregates token features into hyperedge representations, applies lightweight adaptation, and diffuses updates back via hypergraph structure, enabling explicit structural inductive bias while maintaining efficiency. Experiments show consistent performance gains over baseline PEFT methods, especially on tasks requiring structured reasoning.
MetaPS is a simulation-guided framework that enables market agents to adaptively select among programmatic strategies based on market states. It uses simulated markets to generate supervised training data, then selects strategies during inference to produce executable actions. Experiments show MetaPS outperforms fixed strategies and LLM-based agents, with compact models exceeding stronger API models in performance.
P4IR, a two-stage framework, uses supervised fine-tuning and Group Relative Policy Optimization to enhance large language model-based automated code compliance systems. It reduces tree edit and token-level Levenshtein distances by up to 23.8% and 38.6% respectively, outperforming leading LLMs like Claude Opus, GPT-5.2, and GLM-4.7 in zero-shot settings with few-shot prompting, and reduces false positives by a small but statistically significant margin.
Gold Points Sniper (GPS) enables lightweight vision-language models to perform self-guided multimodal reasoning for fine-grained human action understanding. By integrating a Gold Points Extractor, Selective Socratic Questioner, and Semantic Entailment Evaluator, GPS achieves performance comparable to GPT-4o while maintaining superior factual accuracy on CAP benchmark-based instruction-tuning data.
NVIDIA's NeMo AutoModel enables faster fine-tuning of transformer models by automating model selection and optimization. It reduces development time and improves efficiency in training large language models on NVIDIA hardware.
Large language models learn causal structure through a difference-making logic, akin to the experimental method. This approach identifies which word sequences influence outcomes and which do not, using vast text data during training. Architectural features like token embeddings and self-attention support this inductive process by detecting patterns of variation and indifference in language.
DreamUV introduces an end-to-end learning framework that treats UV unwrapping as a generative Flow Matching problem. It learns a mesh-conditioned transport process to generate artist-like UV layouts, with boundary-aware training and Model-in-the-Loop fine-tuning to ensure seam geometry and practical validity. Results show straighter seams, tighter axis-aligned islands, and superior alignment with professional artist preferences.
A fully differentiable emulator of the Atari 2600 VCS is presented, reproducing all 64 ALE games with bit-for-bit accuracy in RAM and screen output. The system enables gradient-based explainable AI by providing a complex, fully known ground truth, with both Julia and JAX implementations validated against a reference emulator and capable of high-throughput differentiable rollouts on GPU.
The paper proposes a self-evolving cognitive framework that uses causal world modeling to enable embodied systems to continuously refine their internal models through interaction. It integrates causal modeling, intervention-driven reasoning, and continual refinement, redefining embodied interaction as an epistemic process for causal discovery and knowledge acquisition. The framework supports a shift from predictive to epistemic intelligence, with a new benchmark for evaluating self-evolving embodied scientific intelligence.
This study compares characters in LLM-generated and human-written stories using narratological dimensions. It finds that while LLMs produce characters with similar basic traits, they lack diversity in complex character features like stylization and wholeness. The research highlights key differences in character depth and variety between human and machine-generated narratives.
PRIME introduces a framework to analyze how large language models handle conflicting instructions by generating calibrated conflicts in response length, format, and reasoning. The study finds that conflict type has a greater impact on model behavior than model size, revealing diverse failure modes across conflict categories. Results highlight the need for conflict awareness and suggest instruction following cannot be reliably assessed through isolated benchmarks alone.
FACTOR introduces an inference-time model that adapts verification criteria based on claim-level uncertainty. It improves factuality and reduces verification cost by dynamically allocating effort to high-risk claims, demonstrating effective and model-agnostic performance on the FactScore benchmark.
VADAOrchestra introduces a neurosymbolic framework that combines LLM-based workflow orchestration with Datalog+/- symbolic reasoning. It enables adaptive, explainable decision-making by incrementally planning workflows and executing logical inference on demand, offering verifiable traces, auditability, and scalability over large datasets.
The author benchmarks LLMs in simulating wetting behaviour using Surface Evolver, a 1992 tool for modeling liquid surfaces. LLMs are evaluated objectively by comparing their generated datafiles against reference implementations, with results showing pass counts and token costs for each model.
SCOPE introduces a framework that refines action plans and evolves symbolic world models in open-ended environments. It combines a Symbolic Execution Simulator and a Self-Adaptive Symbolic Memory to improve plan completeness, perturbation resilience, and cross-task adaptability.
A large language model orchestrates the design of a silicon-on-insulator 2x2 directional coupler by proposing gap values and assessing convergence. The design is validated through eigenmode and FDTD simulations on a common 2D effective-index model, showing a consistent phase offset of 2.837(11) micrometers that is corrected in a closed-loop process. The final device achieves a 50/50 split with a cross fraction of 0.498, within 0.0017 of the target.
Talos is an open-source tool that automates iterative reanalysis of genomic data to identify rare disease diagnoses. It achieved a 90% recovery rate of in-scope diagnoses with only 1.3 candidate variants per patient, and delivered 241 new diagnoses across 5,000 undiagnosed patients, with most new findings emerging within 32 days of evidence publication.
A structured prompt framework improves local LLM accuracy in environmental monitoring by transforming raw sensor data into enriched textual representations. Evaluations on indoor and outdoor datasets show local model accuracy increases from 50.9% to 81.7% indoors and 63.7% to 79.3% outdoors with enriched prompts, while maintaining low latency of nearly 0.22 seconds in no-chain-of-thought mode.
Agentic AI performance degrades exponentially in non-deterministic environments, with k-step success falling as δ^k when per-step determinism δ < 1. The paper introduces a framework linking environment determinism to task success, verifiability, and skill evolution, proposing a Supply Certainty Index and a five-level Determinism Maturity Model. It challenges prevailing views by identifying determinism as a binding constraint across compute, data, embodiment, and alignment.