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Que no, que no hace falta reinventar la rueda cada vez: HASTE llega para que los ingenieros de ML no pierdan el tiempo
IA

Que no, que no hace falta reinventar la rueda cada vez: HASTE llega para que los ingenieros de ML no pierdan el tiempo

arXiv:2606.30911v1 Announce Type: new Abstract: ML engineering agents waste compute rediscovering known techniques because every competition is a cold start. We present HASTE, a hierarchical multi-agent system that organizes cross-competition knowledge into three scope tiers (global, domain, and competition-specific), each coupled to a matching agent level. An orchestrator coordinates domain specialists and promotes learning between tiers via LLM-driven abstraction. A controlled ablation provid

01/07/2026
Meterse en líos legales con un montón de IAs charlando entre sí
Tecnología

Meterse en líos legales con un montón de IAs charlando entre sí

arXiv:2606.30906v1 Announce Type: new Abstract: Artificial Intelligence is increasingly applied to the field of law, and has the potential to increase access to justice. One particular movement that is gaining traction is that of agentic AI, wherein AI agents, based on Large Language Models (LLMs) can take autonomous actions. In particular, multi-agent approaches in the legal domain remain largely unexplored. In this paper, we investigate multi-agent deliberation methods for legal reasoning tas

01/07/2026
Los asistentes virtuales no saben lo que no sabes (y ese es el problema)
IA

Los asistentes virtuales no saben lo que no sabes (y ese es el problema)

arXiv:2606.30863v1 Announce Type: new Abstract: Agents typically assume an expert user -- one with well-formed preferences about what they want -- and default to clarifying questions whenever the task is underspecified. We argue this assumption is unrealistic. Users often lack the domain knowledge to have completely specified preferences; if asked about their preference on some feature, the user may be unable to answer without the agent helping the user to learn some domain knowledge needed to

01/07/2026
Depura prompts como si fueran bugs: la nueva movida de la IA para no perder el culo
IA

Depura prompts como si fueran bugs: la nueva movida de la IA para no perder el culo

arXiv:2606.30840v1 Announce Type: new Abstract: LLM agents are becoming central to information retrieval: they issue retrieval queries, synthesize answers, and increasingly serve as judges for IR evaluation. Improving the prompts that control these agents is an optimization problem, but in applied IR settings it often looks less like blind search and more like debugging. Engineers need to know which behavior failed, which nearby behavior still worked, what distinguishes the two, and whether a p

01/07/2026