4arXiv cs.LG (Machine Learning)·
GARI: Generator-Aligned Representation Interface for soft equivariance in generic sequence backbones
Researchers introduce the Generator-Aligned Representation Interface (GARI), a design principle that exposes transformation generators to generic sequence backbones without requiring group-specific architectural changes. The framework formalizes soft equivariance via a probe-specific residual metric and instantiates it as GARI-Net, validated on genomic sequences, images, and 3D point clouds across symmetry transformations including reversal, rotation, and reflection. A companion diagnostic metric, Direct Equivariance Error (DEE), enables frozen-checkpoint evaluation of representation equivariance. The work offers a portable alternative to hard-equivariant architectures, making symmetry structure learnable and measurable across modalities.
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SGA: Plug-and-play geometric verification module for LLM-generated educational animations
Researchers introduce the Symbolic Geometric Agent (SGA), a modular component that intercepts LLM-generated Manim animation code, extracts symbolic scene graphs via partial execution, and applies targeted refinement to resolve spatial conflicts and occlusions. They also propose MVQS (Manim Visual Quality Score), a deterministic rendering-free proxy metric for spatial integrity. Evaluated on the MMMC-Code benchmark across four LLM backbones and two agentic pipelines, SGA achieves a 16.1% relative improvement in MVQS over baseline. The work addresses a gap in LLM-driven code generation pipelines where geometric correctness is underserved.
SIGA: Self-evolving grounding adapters enable coding agents to operate scientific simulators
SIGA (Simulator-Interface Grounding Adapter) is a lightweight adapter framework that equips general-purpose coding agents with the executable contracts needed to configure and run specialized scientific simulators. Evaluated primarily on GEOS (a multiphysics subsurface simulator), SIGA achieves a ~36x wall-clock speedup over human experts and improves TreeSim scores from 0.720 to 0.789 on held-out tasks, with self-evolution via trajectory rewriting yielding further gains. The system also transfers to OpenFOAM and LAMMPS, revealing that the dominant grounding mechanism (validation vs. memory/retrieval) shifts depending on the interface type. The work frames simulator setup as an agent-tool interface grounding problem, offering a generalizable pattern for deploying coding agents on domain-specific software.
GaP: Graph-as-Policy multi-agent harness for reliable robot automation via self-learning computation graphs
Researchers introduce Graph-as-Policy (GaP), a multi-agent coding harness that generates directed computation graphs combining perception, planning, and control nodes for robotic 'Variational Automation' tasks — those with high variation in object geometry and pose. GaP uses an internal simulation environment to rehearse and iteratively refine graph structures in parallel, improving success rates without relying solely on model-free policies. Evaluation across 8 new benchmarks (4 simulated, 4 real-world) shows significant outperformance over baselines. The work bridges agentic coding systems with interpretable robot programming and Task and Motion Planning.
Faster Assisted Generation Support for Intel Gaudi
Hugging Face has published a blog post detailing assisted generation (speculative decoding) support optimized for Intel Gaudi accelerators. The post covers implementation details and performance improvements achieved by running assisted/speculative decoding on Gaudi hardware. This represents an infrastructure and inference optimization development relevant to non-NVIDIA AI accelerator deployment.
DualG-MRAG: Dual-tier graph architecture decouples macro-reasoning and micro-matching for multimodal RAG
A new arXiv preprint introduces DualG-MRAG, a framework for Multimodal Retrieval-Augmented Generation that separates global structural reasoning (Macro Graph) from fine-grained local evidence matching (Micro Graph) to address retrieval noise in multi-hop reasoning tasks. The system uses a GNN-based retriever with query-driven message passing and a dynamic programming decoding mechanism that extracts explicit reasoning paths from the GNN's forward pass rather than feeding isolated document chunks to the generative model. Experiments show improvements over baselines on evidence recall and complex QA accuracy. The work targets a known failure mode of graph-enhanced MM-RAG systems where fine-grained visual features cause graph explosion and noise.
IVGT: Implicit Visual Geometry Transformer for Neural Scene Representation
IVGT is a new neural architecture that implicitly models continuous 3D geometry from unposed multi-view images without requiring explicit pointmap regression. It learns a continuous neural scene representation in a canonical coordinate system, supporting SDF-based surface queries and color prediction via lightweight decoders. The model is trained with multi-dataset joint optimization using 2D supervision and 3D geometric regularization, achieving strong generalization across mesh reconstruction, novel view synthesis, depth/normal estimation, and camera pose estimation tasks.
Comparative framework for RAG variants: when GraphRAG and Agentic RAG are actually needed
A new arXiv preprint introduces a systematic evaluation framework comparing nine standardized RAG scenarios across regular RAG, GraphRAG, Modular RAG, and Agentic RAG on semi-structured knowledge bases. The authors propose a novel context engineering method that reduces token usage by 19–53% for GraphRAG and Agentic RAG by addressing context/memory overflow. A key finding is a 'retrieval-generation gap' where expanded retrieval does not proportionally improve generation quality, suggesting retrieval-oriented metrics overstate the benefits of advanced retrieval. The work targets practitioners building production RAG systems and provides data-driven guidance on when to use each variant.
GGRO: Gradient-Guided Reward Optimization for inference-time LLM alignment
Researchers introduce Gradient-Guided Reward Optimization (GGRO), an inference-time alignment method that uses gradient signals from a reward model to inject 'nudging tokens' at high-uncertainty decoding steps, rather than relying on sampling-intensive re-ranking approaches like Best-of-N. The method monitors token-level entropy to detect distribution drift and steers generation trajectories directly, claiming improved robustness to reward hacking with minimal computational overhead. Experiments show gains across safety, helpfulness, and reasoning benchmarks compared to standard inference-time alignment baselines.