Papers for

game engine programmers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Selective neural networks improve 3D Gaussian splatting images

Is Gaussian Splatting Becoming Neural Again? A Taxonomy and Controlled Study of Learned Parameterization

Abstract: Three-dimensional Gaussian Splatting (3DGS) combines explicit primitives with efficient rasterization, yet recent systems increasingly use neural networks to generate or share Gaussian parameters. We characterize this trend along five axes: attribute decoding, spatial sharing, view-conditioned decoding, topology generation, and amortized inference. An analysis of 19 representative methods shows that these choices address different limitations and cannot be reduced to a binary neural label. We also isolate three forms of neural parameterization in a controlled mip-NeRF 360 study. Sharing appearance and opacity improves reconstruction quality, while decoding geometric structure offers no further gain. The evidence favors selective neuralization: shared functions help when they capture reusable correlations without sacrificing the local geometric freedom of explicit splats.

Fri 11 SeptArtificial Intelligence
The gist
Creating 3D images using tiny Gaussian blobs is fast but has limits. The authors looked at how adding neural networks to control these blobs helps improve picture quality. They found that sharing certain features like appearance and opacity through neural networks makes the image better. However, using neural networks to control the shape or position of these blobs doesn’t add benefit. This shows that mixing simple shapes with selective neural control works best.
Open → 2609.12395v1

Adaptive control improves interaction limits in ai agents with fewer tokens

Elastic Horizon: Discovering the Effective Interaction Frontier in Agentic Reinforcement Learning

Abstract: Scaling the interaction horizon-the maximum number of environment interactions per episode-improves LLM agents on long-horizon tasks, and curriculum-based methods that progressively expand the horizon outperform fixed-horizon alternatives. However, existing schedules are open-loop: they monotonically increase the horizon until a manually specified maximum, with no mechanism to detect when further expansion stops helping. We propose the effective interaction frontier hypothesis: a dynamic boundary beyond which additional interactions yield diminishing returns while cost grows linearly. We then introduce Elastic Horizon, a closed-loop controller that tracks this boundary via the 90th percentile of successful trajectory lengths. On AppWorld and BFCL, fixed-horizon sweeps reveal clear saturation plateaus; Elastic Horizon stabilizes the horizon inside the saturation band from both under- and over-capacity initializations, attains the best success rates across 7B and 14B backbones, and saves up to 25% of per-step trajectory tokens. Our work shifts the paradigm from how to scale interaction horizons to when to stop scaling.

Mon 7 SeptArtificial Intelligence
The gist
The paper looks at how AI agents that learn by interacting with environments can perform better by adjusting the number of interactions they have during a task. Instead of just increasing interactions up to a fixed maximum, the authors propose a method called Elastic Horizon that adapts the interaction length based on how much improvement is still happening. They find that beyond a certain point, more interactions don't help much but cost more, so their method stops increasing when it detects this. This leads to better success rates and saves computational resources.
Open → 2609.07247v1