The paper discusses how large language model (LLM) agents can perform epistemic actions—actions that improve the evidence base before making a decision. It identifies three modes of such actions: acquiring missing evidence, transforming available evidence, and probing the system to generate revealing responses. The authors introduce the concept of epistemic scaffolding, referring to interfaces, tools, and environments that enable and audit these actions, arguing that agent design must explicitly address how decision-ready evidence is produced.
By Yizhi Liu, Balaji Padmanabhan, Siva Viswanathan
arXiv:2609.01588v1 Announce Type: cross
Abstract: Writing involves diverse cognitive activities, from ideation to revision, and writers' needs vary across individuals and moments. Proactive AI promis...
By Chao Zhang, Abe Davis, Chih-Wei Chen, Chin-Chia Hsu
Conversational agents are increasingly embedded in human collaborative work, yet they remain fundamentally passive and reactive: they respond to explicit user requests rather than proactively recognizing moments when a team would benefit from timely intervention as human collaborators often do. This reactive design substantially limits the use of agents as active participants in multi-user collaboration, where disagreements, ambiguous goals, forgotten constraints, underspecified plans, discussion loops, and imbalanced participation can gradually undermine group progress.
The paper introduces a framework for designing proactive large‑language‑model agents, centered on three joint principles—Task Capability, Temporal Allocation, and Trust—alongside a five‑dimensional design space. It proposes PROACTIVITY‑GYM, a simulation testbed for evaluating proactive assistance across multi‑day scenarios, and presents empirical findings that highlight performance gaps and the importance of aligning interventions with user trust. Human studies show that misaligned interventions can sharply reduce trust, even when outcomes are correct, underscoring the need for careful joint optimization of the 3T principles.
By Jio Oh, Seunghyun Do, Young-Jun Lee, Steven Euijong Whang, Dongyeop Kang
arXiv:2608.22266v1 Announce Type: new
Abstract: In the context of information seeking, conversational agents are undergoing an evolution from reactive tools to proactive, personalized assistants. A c...
By Zhihong Cao, Chen Huang
arXiv:2607. 11053v1 Announce Type: cross Abstract: Fruitful collaborations rely on cooperative communications, including of contextual cues to incorporate into reasoning.
By Hannah VanderHoeven, Abhijnan Nath, Nikhil Krishnaswamy