Skip to content

Asking for What Was Never Requested: Horizontal and Vertical Proactivity in Agents research paper by IBM, 2026

IBM · Sep 29, 2026 · Agents and evaluation · 39 upvotes · unverified 6 days ago

Read on arXiv

What it shows

An agent that uses tools typically responds to what the user explicitly asks, yet completing the task may require information the user never requested.

By Ido Levy, Asaf Yehudai, Segev Shlomov and 2 more · arXiv 2609.37236 · PDF · Code

UnverifiedHugging Face's summary; not yet checked by hand.

More from IBM

All 4
Topic
PaperCitations
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic CapabilitiesGemini 2.X model family, including Gemini 2.5 Pro and Flash, offers superior coding, reasoning, and multimodal understanding capabilities across a range of computational efficiencies.Google · Jul 2025 · Unverified1 year ago4,244
DeepSeek-V3.2: Pushing the Frontier of Open Large Language ModelsDeepSeek-V3.2 introduces DeepSeek Sparse Attention and a scalable reinforcement learning framework, achieving superior reasoning and performance compared to GPT-5 and Gemini-3.0-Pro in complex reasoning tasks.DeepSeek · Dec 2025 · Unverified10 months ago784
Aya Model: An Instruction Finetuned Open-Access Multilingual Language ModelAya, a multilingual generative language model supporting over 50% lower-resourced languages, excels in both generative and discriminative tasks across 99 languages, and provides extensive evaluations and open-source resources.Cohere · Feb 20242 years ago410
WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?WorkArena and BrowserGym evaluate large language model-based agents' ability to perform enterprise software tasks, revealing gaps in current agent capabilities and differences between open and closed-source LLMs.ServiceNow · Mar 20242 years ago387
Rubrics as Rewards: Reinforcement Learning Beyond Verifiable DomainsRubrics as Rewards (RaR) framework uses structured rubrics as interpretable reward signals for on-policy training, improving performance in real-world reinforcement learning tasks with subjective criteria.Scale AI · Jul 20251 year ago343
SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?SWE-Bench Pro is a challenging benchmark for coding models, featuring complex, enterprise-level problems that require substantial code modifications, with performance evaluations showing significant limitations in current models.Scale AI · Sep 20251 year ago270
About this paper
Authors
Ido Levy, Asaf Yehudai, Segev Shlomov and 2 more
arXiv
2609.37236 · PDF
Citations
Not counted yet · Semantic Scholar
Upvotes
39 · Hugging Face
Code
github.com/dolev31/ProactiveInquirer
Lab
IBM · on Companies · on Acquisitions · on Quarterly · on Paydays · on TechConf

Changes

What changed
Upvotes38 to 39 (+1)Oct 5, 2026today
New paperFound by the weekly scan, unverifiedOct 5, 2026today

New papers by email

Monday afternoons, only in weeks with new papers from the labs.

Double opt-in. Unsubscribe any time.