Intermediate Semantic Representation
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Related Articles from SNS
A Normative Intermediate Representation for ASP-Based Compliance Reasoning
arXiv:2606.04619v1 Announce Type: new Abstract: We propose MONIR, a Modalized-Output Normative Intermediate Representation for ASP-based compliance reasoning. Its core fragment has a staged operational semantics, while MONIR-ASP provides an executable compilation and extensions for external functions, temporal rules, and stable-model reasoning. We instantiate the framework on Chinese ADAS regulations and standards with an LLM-assisted pipeline.
Imagine Before You Draw: Visual Prompt Engineering for Image Generation
arXiv:2606.04457v1 Announce Type: new Abstract: Incorporating visual semantic representations as an intermediate step before image generation can reduce the modeling difficulty between text and images, thereby improving generation quality. Recent works such as X-Omni and BLIP3o-Next have explored this direction, but they typically use a two-stage external pipeline: a separate autoregressive model first generates semantic tokens, which are then fed as conditioning to an independent diffusion...
Formal verification of the S-two AIR
arXiv:2606.04311v1 Announce Type: new Abstract: StarkWare's S-two prover provides an efficient means for establishing, on blockchain, that a program written in the Cairo virtual machine language runs to completion. The latter claim is encoded by an algebraic intermediate representation (AIR) that captures the semantics of the Cairo language. The AIR asserts the existence of tables of values from a finite field satisfying certain algebraic constraints.
Native3D: End-to-End 3D Scene Generation via Unified Mesh-Texture Modeling and Semantic Alignment
Announce Type: new Abstract: This paper presents Native3D, the first end-to-end 3D scene generation framework that completely bypasses 2D intermediate representations. Traditional approaches typically require adapting 3D representations to the 2D domain to leverage pre-trained diffusion models, which inevitably introduces domain adaptation issues including geometric structural distortion and texture detail degradation. To address these limitations, we design a unified mesh-texture joint...
Visual Instruction Tuning Aligns Modalities through Abstraction
arXiv:2606.03871v1 Announce Type: new Abstract: Visual instruction tuning effectively adapts a pre-trained Large Language Model (LLM) to process image information alongside text. Yet, it remains unclear how visual features are embedded into the layer-wise hierarchy of abstractions of the LLM backbone. Across a diverse set of vision-language architectures, we show that instruction tuning primarily serves as a bridge, embedding visual features directly into the intermediate semantic layers of...
Semantic Triplet Restoration: A Novel Protocol for Hierarchical Table Understanding in Large Language Models
arXiv:2605.31550v1 Announce Type: new Abstract: Table question answering requires models to recover semantic relations encoded implicitly by two-dimensional layout, merged cells, and hierarchical headers. Current pipelines typically use HTML or Markdown as intermediate table representations, but these layout-oriented serializations introduce markup overhead and require large language models to infer header-cell alignments from row and column spans. We propose Semantic Triplet Restoration...
Beyond Skeletons: Learning Animation Directly from Driving Videos with Same2X Training Strategy
arXiv:2606.06903v1 Announce Type: new Abstract: Human image animation aims to generate a video from a static reference image, guided by pose information extracted from a driving video. Existing approaches often rely on pose estimators to extract intermediate representations, but such signals are prone to errors under occlusion or complex poses. Building on these observations, we present DirectAnimator, a framework that bypasses pose extraction and directly learns from raw driving videos.
PHASOR: Phase-Anchored Universal Action Representations for Humanoid Embodiments
Announce Type: replace Abstract: Learning a good action embedding space is fundamental to scalable robot policy learning, yet existing methods treat action latents as task-specific intermediates rather than first-class representations. The resulting latents are unstructured, embodiment-specific, and weakly tied to motion semantics, limiting interpretability, controllability, and transferability across robots. We position the action embedding space itself as a first-class design target, with...
PHASOR: Phase-Anchored Universal Action Representations for Humanoid Embodiments
arXiv:2606.01851v1 Announce Type: new Abstract: Learning a good action embedding space is fundamental to scalable robot policy learning, yet existing methods treat action latents as task-specific intermediates rather than first-class representations. The resulting latents are unstructured, embodiment-specific, and weakly tied to motion semantics, limiting interpretability, controllability, and transferability across robots.
Port React Compiler to Rust
[compiler] Port React Compiler to Rust#36173 This is an experimental, work-in-progress port of React Compiler to Rust. Key points: - Work-in-progress - we are sharing early, prior to testing internally at Meta, to get feedback from partners in parallel with continued development.