The easiest way to read a daily research digest is as a stack of disconnected papers. That is usually the least useful way to read it. The better move is to look for the technical directions that keep surfacing, the problems researchers are taking more seriously, and the kinds of systems that look increasingly deployable.

This brief is a synthesis of the digest rather than a direct dump of every item. The goal is to surface what matters for people building AI systems, workflow automation, internal assistants, and production infrastructure.

Where the structure showed up

The strongest signal in this digest is that multimodal work is becoming harder to separate from the orchestration layers around it. More of the useful progress is happening in the interfaces between perception, reasoning, tool use, and evaluation.

That matters because production systems are rarely judged on one capability in isolation. They are judged on whether the surrounding control surface turns model ability into repeatable behavior.

What builders should pay attention to

For teams shipping internal assistants or workflow systems, the practical gain is not just richer inputs. It is better system structure: clearer execution steps, tighter observation loops, and fewer hidden assumptions.

That points toward products that are narrower, better instrumented, and more explicit about how they operate when the environment gets messy.

Paper summaries

Below are the individual papers and a fuller summary of what each one is doing, what looks new, and why it may matter, followed by direct source links.

1. OceanXL: Large-scale Underwater 3D Gaussian Splatting via Block Partitioning and Adaptive Pruning

We propose OceanXL, a fast and scalable 3DGS-based framework for large-scale underwater reconstruction. We also introduce a large-scale underwater dataset covering diverse marine environments. OceanXL is best read as new data infrastructure in 3D and visual generation.

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2. Jev-Mobile: Jev as an Executor for Mobile GUI Agents

We introduce Jev-Mobile, which shifts this paradigm to low-frequency VLM planning and high-frequency lightweight execution: the VLM specifies local goals, the accessibility tree defines a structured executable action space, and Jev, a fast typed decision…. These results show that decoupling high-level VLM reasoning from low-level action execution can substantially improve mobile GUI agent efficiency while maintaining competitive task performance. Jev-Mobile is best read as an implementation framework in systems efficiency.

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3. Industrial Anomaly Detection via Defect-Grounded Reasoning in Visual Latent Space

To address these, we propose Anomaly-LR, a defect-grounded latent reasoning framework that first forms a global understanding of the input and then progressively refines anomaly-relevant representations directly in the visual latent space. We further construct IAD-LR-22K, the first IAD instruction dataset designed for latent reasoning, containing 22,228 image-question instances from 4,523 industrial images, with global textual reasoning traces and region-level visual annotations. Industrial Anomaly Detection via Defect-Grounded is best read as new data infrastructure in agent debugging and observability.

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4. AdaHVLA: Adaptive Harnesses for Long-Horizon Vision-Language-Action Execution

To bring these complementary capabilities together, we introduce AdaHVLA, an adaptive harness that refines code-based coordination policies through robot experience to better align agent reasoning and memory with VLA execution. Title: AdaHVLA: Adaptive Harnesses for Long-Horizon Vision-Language-Action Execution Base summary: Vision-language-action (VLA) models offer strong local control and instruction following but often struggle with long-horizon tasks requiring persistent memory…. AdaHVLA is best read as better debugging hooks in robotics and embodied perception.

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5. When Can Agents Forget Their Reasoning? ICLR for Long-Horizon Agent Context Compression

We propose Interaction Aware Compression for Long Horizon Reasoning (ICLR), a training free online method that ranks reasoning blocks using frozen proxy entropy while preserving actions, tool calls, and observations. ICLR for Long-Horizon Agent Context Compression Base summary: Long horizon language model agents continually accumulate reasoning history, increasing context length and inference cost even after earlier decisions have been executed and observed. When Can Agents Forget Reasoning is best read as better debugging hooks in agent workflows.

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6. PolyUMI: Accessible Visual-Tactile-Audio Data Collection for Object Inference and Manipulation

Experiments spanning object inference, slip control, and contact-rich manipulation show that touch and audio reveal task-relevant information beyond vision and that VisTA is competitive with or outperforms existing multimodal policies. To effectively use these heterogeneous observations, we further introduce VisTA, a token-level multimodal policy that integrates information across sensors and time to predict contact-aware robot actions. PolyUMI is best read as an implementation framework in multimodal perception.

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7. SARFusion: Scene-Aware Routing Fusion for Robust Camera-LiDAR 3D Object Detection

However, existing fusion detectors often establish strong inter-modality dependencies by decoding object queries from tightly coupled multimodal representations. To bridge this gap, we reformulate robust camera-LiDAR fusion as a scene-aware branch routing problem and propose SARFusion, a robust 3D object detector. SARFusion is best read as better debugging hooks in 3D and visual generation.

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References