Publications of the month - July 2026

The Premier Conference & Exhibition on Computer Graphics & Interactive Techniques (SIGGRAPH 2026), July 2026, Los Angeles (USA)

• Learning-based Sparse Signed Distance Field Super-Resolution
  Sagar Panwar, Nissim Maruani (AI Cluster Ph.D. student), Céline Loscos, Mathieu Desbrun, Pierre Alliez (AI Cluster Chairholder)

Abstract: Signed Distance Fields (SDFs) are a powerful volumetric representation for 3D geometry. Recent advances in surface generation from SDFs increasingly rely on learnable surface representations and direct supervision on meshes. In this work, we challenge this trend and show that high-quality surface reconstruction can instead be achieved by learning to refine the volumetric signal itself. We present SuperSDF, a learning-based approach for sparse SDF super-resolution that operates directly in SDF space, without introducing any auxiliary surface representation or mesh-level supervision. Using a sparse voxel neural network restricted to a narrow band near the surface, our method predicts high-resolution signed-distance values from coarse inputs in a scalable and resolution-agnostic manner. Standard isosurface extraction algorithms can then process the resulting super-resolved SDFs, yielding accurate and detailed surface meshes. Our results demonstrate that learning-based SDF upsampling alone is sufficient to recover fine geometric details that are missed by classical interpolation and prior reconstruction methods. Compared to state-of-the-art ML approaches, our method produces higher-fidelity surfaces at a fraction of the computational cost and scales to volumetric resolutions previously out of reach.

Conference on Language Modeling (COLM2026), October 2026, San Francisco (USA)

• Constraint decay: The Fragility of LLM Agents in Backend Code Generation
  Francesco Dente, Dario Satriani, Paolo Papotti (AI Cluster Chairholder)

Abstract: Large Language Model (LLM) agents demonstrate strong performance in autonomous code generation under loose specifications. However, production-grade software requires strict adherence to structural constraints, such as architectural patterns, databases, and object-relational mappings. Existing benchmarks often overlook these non-functional requirements, rewarding functionally correct but structurally arbitrary solutions. We present a systematic study evaluating how well agents handle structural constraints in multi-file backend generation. By fixing a unified API contract across 80 greenfield generation tasks and 20 feature implementation tasks spanning eight web frameworks, we isolate the effect of structural complexity using a dual evaluation with end-to-end behavioral tests and static verifiers. Our findings reveal a phenomenon of constraint decay: as structural requirements accumulate, agent performance exhibits a substantial decline. Capable configurations lose 30 points on average in assertion pass rates from baseline to fully specified tasks, while some weaker configurations approach zero. Framework sensitivity analysis exposes significant performance disparities: agents succeed in minimal, explicit frameworks (e.g., Flask) but perform substantially worse on average in convention-heavy environments (e.g., FastAPI, Django). Finally, error analysis identifies data-layer defects (e.g., incorrect query composition and ORMruntime violations) as the leading root causes. This work highlights that jointly satisfying functional and structural requirements remains a key open challenge for coding agents.

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