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.

Why operations kept showing up

The best work in this digest assumed that real systems fail in ordinary ways: context gets messy, dependencies drift, and infrastructure limits shape what is actually possible.

That is a healthier direction than treating deployment as a final wrapper around a benchmark win.

What builders can take from it

For people running AI inside businesses, the useful advances are the ones that change reliability, monitoring, evaluation, or the cost of keeping a system healthy over time.

Those details are less glamorous than raw capability claims, but they are the details that decide whether a system survives contact with operations.

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. REA: A Routed Embodied Agent Runtime for Asynchronous Brain-Cerebellum Control

To address this, we propose REA (Routed Embodied Agent), a unified runtime where a deliberative System 2, an embodied router System 1 and reactive System 0 policies cooperate under a single golden rule - no running policy waits on the brain. Instead of querying the brain at each failure, REA employs a route memory that stores each policy's measured success ahead of time, a boundary-triggered judge gate that consults the brain on policy execution, and a shared-budget escalation ladder for bounded…. REA is best read as an implementation framework in robotics and embodied perception.

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2. Real Computation or Merely Naming? A Pre-Registered Mechanism Audit of 25 Cognitive-Architecture Modules in an LLM-Based Agent System

A Pre-Registered Mechanism Audit of 25 Cognitive-Architecture Modules in an LLM-Based Agent System Base summary: Agent systems built on local large language models are increasingly marketed through the vocabulary of cognitive architecture: modules named…. We audit a system claiming 25 cognitive mechanisms, pre-register falsifiable predictions for 11 modules and execute the interventions, replicate the protocol on the IIT-phi core of a second, independent system with its own pre-committed falsification…. Real Computation Merely Naming Pre-Registered is best read as an implementation framework in agent workflows.

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3. META-Robot: Self-Cognition and Capability Scaling in Agentic Robots

We introduce META-Robot, a framework that couples self cognition with capability scaling by expanding the criteria through which an agent evaluates and develops its skills. Title: META-Robot: Self-Cognition and Capability Scaling in Agentic Robots Base summary: LLM-based robotic agent systems can generate programs for robot control, yet their generalization to new tasks often depends on elaborate scaffolding. META-Robot is best read as a stronger benchmark in agent workflows.

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4. RoboArchitect: Automating the Design of Robotic Agents with Persistent Memory

The gains are largest where the benchmark is far from the policy's training data: wrapping a general-purpose policy, the evolved harness yields a specialist that often outperforms even the finetuned version. Yet today these harnesses are built by hand and redesigned for each new robot or benchmark. RoboArchitect is best read as a stronger benchmark in robotics and embodied perception.

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5. Aligning MLLMs with the Latent Structure of Human Cognition via Behavior-Derived Semantic Dimensions

In this work, we propose a novel framework to align MLLMs with the multidimensional mental representations underlying human similarity judgements. We build on a previously established behavior-derived semantic embedding space, in which objects are represented along 66 interpretable dimensions derived from 4.70 million human Odd-One-Out (O1O) judgements. Aligning MLLMs Latent Structure Human is best read as a stronger benchmark in developer tooling.

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6. When Depth Lies: Benchmarking Vision-Language Models on Mirror-Induced RGB-D Ambiguity

Then, we curate a benchmark named MIRA-Bench (Mirror-Induced Reasoning Ambiguity Benchmark), comprising 14,140 QA pairs across eight sub-tasks organized into three cognitive levels: Reflection-aware Perception, Spatial Reasoning, and Scene-level…. In this work, we introduce a mirror-centric RGB-D dataset from reflective scenes to evaluate the spatial reasoning capabilities of state-of-the-art VLMs in these scenarios. When Depth Lies is best read as a stronger benchmark in 3D and visual generation.

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7. BlindToChange: AI Agents Can Find Changes When Asked But Do They Look When Needed?

Our benchmark highlights, and tracks, a new axis of progress, wherein systems account for recent information that is not explicitly mentioned in the user request. To highlight such gaps, we introduce BlindToChange, a benchmark that measures whether agents proactively discover recent events that affect their responses. BlindToChange is best read as a stronger benchmark in agent workflows.

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References