arxiv arXiv cs.AI · 14h ago

Sparsity-Storage-Accuracy Tradeoff in Parsimoniously Activated Dictionary Learning

Parsimoniously activated dictionary learning (PADL) establishes a structured generative model with auxiliary latent variables, enabling maximum a posteriori estimation. This framework provides generalization guarantees and an analytical characterization of the tradeoff between sparsity, storage cost, and reconstruction accuracy, allowing data-driven hyperparameter estimation. The resulting algorithm achieves better reconstruction performance and accelerates inference in vision-language models.

arxiv arXiv cs.AI · 14h ago

First-Token Broadcasters in Transformers: Language Identity and Robustness

LIHA reveals a small set of first-token broadcaster heads in GPT-2 that persistently attend to the initial prompt token, driving language switches. Instruction tuning reorganizes these circuits, concentrating language identity at early layers, as seen in Qwen2.5-1.5B-Instruct and confirmed in Chinese and Russian language handling at layer 0.

arxiv arXiv cs.AI · 14h ago

ARIA: A Causal-Aware Framework for Rescuing LLM Reasoning

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.

arxiv arXiv cs.AI · 14h ago

HyperAdapter: Structured Hyperedge Adaptation for Vision Transformer Fine-Tuning

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.

arxiv arXiv cs.AI · 14h ago

MetaPS: Adaptive Strategy Selection for Market Agents

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.

arxiv arXiv cs.AI · 14h ago

P4IR Framework Improves LLM-Based Code Compliance Accuracy

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.

arxiv arXiv cs.AI · 14h ago

Gold Points Sniper: Self-guided Visual Reasoning for Fine-grained Action Understanding

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.

lab Hugging Face Blog · 14h ago

NVIDIA NeMo AutoModel Speeds Up Transformer Fine-Tuning

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.

arxiv arXiv cs.AI · 15h ago

LLMs Use Difference-Making Logic to Learn Causal Structure

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.

arxiv arXiv cs.AI · 15h ago

DreamUV: End-to-End Flow Matching for Artist-like UV Unwrapping

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.

arxiv arXiv cs.AI · 15h ago

A Differentiable Atari VCS for Explainable AI

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.

arxiv arXiv cs.AI · 15h ago

Self-Evolving Cognitive Framework for Embodied Scientific Intelligence

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.

arxiv arXiv cs.AI · 15h ago

Character Variety in LLM-Generated Stories

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.

arxiv arXiv cs.AI · 16h ago

PRIME: Evaluating Prompt Resolution in Conflicting Instructions

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.

arxiv arXiv cs.AI · 16h ago

FACTOR Enables Adaptive Verification for Factuality in Long-Form Generation

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.

arxiv arXiv cs.AI · 16h ago

VADAOrchestra: Neurosymbolic Orchestration of Adaptive Reasoning Workflows

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.

media r/LocalLLaMA · 16h ago

My micro-benchmark: how good are LLMs at simulating wetting behaviour?

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.

arxiv arXiv cs.AI · 16h ago

SCOPE: Self-Adaptive Symbolic Planning for Open-Ended Environments

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.

arxiv arXiv cs.AI · 16h ago

LLM-Orchestrated Agent for SOI Directional Coupler Design

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.

lab Microsoft Research Blog · 17h ago

Talos: Automated Genomic Reanalysis for Rare Disease Diagnosis

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.