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The Long-Horizon Task Mirage? Diagnosing Where and Why Agentic Systems Break

BACKGROUND
5 SIGNALSFIRST DETECTED 15 April 2026
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6 Oct
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15 Apr
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Other feeds and industry voices
13 May
The Evaluation Differential: When Frontier AI Models Recognise They Are Being Tested

arXiv:2605.11496v1 Announce Type: new Abstract: Recent published evidence from frontier laboratories shows that contemporary AI models can recognise evaluation contexts, latently

REPORT · WHEN · ArXiv CS.AIOPEN STORY
13 Apr
DRBENCHER: Can Your Agent Identify the Entity, Retrieve Its Properties and Do the Math?

arXiv:2604.09251v1 Announce Type: new Abstract: Deep research agents increasingly interleave web browsing with multi-step computation, yet existing benchmarks evaluate these

REPORT · WHAT · ArXiv CS.AIOPEN STORY
2 May
In-Context Prompting Obsoletes Agent Orchestration for Procedural Tasks

arXiv:2604.27891v1 Announce Type: new Abstract: Agent orchestration frameworks -- LangGraph, CrewAI, Google ADK, OpenAI Agents SDK, and others -- place an external orchestrator

REPORT · WHAT · ArXiv CS.AIOPEN STORY
2 Apr
Brevity Constraints Reverse Performance Hierarchies in Language Models

arXiv:2604.00025v1 Announce Type: cross Abstract: Standard evaluation protocols reveal a counterintuitive phenomenon: on 7.7% of benchmark problems spanning five datasets, larger

REPORT · HOW · ArXiv CS.AIOPEN STORY
11 May
Beyond the Black Box: Interpretability of Agentic AI Tool Use

arXiv:2605.06890v1 Announce Type: new Abstract: AI agents are promising for high-stakes enterprise workflows, but dependable deployment remains limited because tool-use failures

REPORT · WHAT · ArXiv CS.AIOPEN STORY
27 Apr
Estimating Tail Risks in Language Model Output Distributions

arXiv:2604.22167v1 Announce Type: cross Abstract: Language models are increasingly capable and are being rapidly deployed on a population-level scale. As a result, the safety of

REPORT · WHEN · ArXiv CS.AIOPEN STORY
27 Apr
Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond

arXiv:2604.22748v1 Announce Type: new Abstract: As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment

REPORT · WHAT · ArXiv CS.AIOPEN STORY
9 Apr
Scaling Coding Agents via Atomic Skills

arXiv:2604.05013v1 Announce Type: cross Abstract: Current LLM coding agents are predominantly trained on composite benchmarks (e.g., bug fixing), which often leads to

REPORT · HOW · ArXiv CS.AIOPEN STORY
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