arXiv:2607. 11053v1 Announce Type: cross Abstract: Fruitful collaborations rely on cooperative communications, including of contextual cues to incorporate into reasoning.
arXiv:2603. 19997v2 Announce Type: replace Abstract: We investigate the separation of literal interpretation from contextual inference in a collaborative block-building tasks, where an agent must resolve underspecified instructions using context.
arXiv:2608.22152v1 Announce Type: new Abstract: Multi-agent systems built from large language models are deployed widely, yet how much performance is lost when two LLMs must coordinate rather than ac...
arXiv:2607. 11363v1 Announce Type: cross Abstract: Text-based evaluations of Theory of Mind (ToM) in Large Language Models (LLMs) often involve cognitive tests akin to the Sally-Anne task that can be gamed due to exposure to relevantly similar tasks in pre-training and do not obviously test models' functional ToM abilities in ways that generalize to naturalistic settings.
Consilience is an inference‑time orchestration framework that steers and certifies communication among multi‑agent large language models in hidden‑profile settings. It summarizes each discussion turn with a compact state of uncertainty, disagreement, evidence gain, redundancy, and premature consensus, then selects a communication intervention (challenge, clarify, seek evidence, or route) and speaker. A round‑wise conformal calibration procedure guarantees that the controller’s proposed action has bounded one‑step regret with high probability, and an acceptance mechanism enforces this guarantee for the executed action. Experiments on HiddenBench‑style tasks show that Consilience improves decision accuracy and communication efficiency over fixed and unstructured protocols, sometimes outperforming a full‑information baseline.
arXiv:2606. 20245v1 Announce Type: new Abstract: Large language models (LLMs) have achieved strong performance across a wide range of language-based tasks by leveraging both extensive parametric knowledge and in-context learning ability, enabling them to incorporate external information provided in the input prompt.
arXiv:2609.17496v1 Announce Type: new Abstract: LLM assistants are widely used for daily social advice, yet evaluating their social reasoning in such consultation settings remains challenging since (...
The paper compares human group discussions with large language model (LLM) deliberation traces on various reasoning tasks, finding that both humans and LLMs exhibit an assembly bonus asymmetry where discussion benefits the average member more than the best initial member. While LLM groups mirror some outcome-level patterns of human deliberation, they differ in process-level behaviors: they tend to follow majorities, surface less unique information, and converge earlier. Interventions inspired by human group‑decision research yield modest outcome improvements but do not eliminate coordination bottlenecks.
arXiv:2606. 06388v1 Announce Type: new Abstract: Recent advances in LLM agents have enabled complex cognitive capabilities, such as multi-step reasoning, planning, and tool use, that increasingly position these agents as human collaborators.
arXiv:2608. 08210v1 Announce Type: new Abstract: Collaborative dialogue can end with apparent agreement while participants still differ on goals, assumptions, or execution plans, creating an \textbf{illusion of alignment (IoA)}.
arXiv:2609.39727v1 Announce Type: new Abstract: Cooperative language-model agents must coordinate over long horizons and adapt to changing environments and to partners with unfamiliar conventions, ye...
The paper investigates how Large Language Model (LLM) agents can collaborate on a shared task under information asymmetry, using a table‑top version of Einstein Puzzles. It introduces a fine‑tuning‑plus‑verifier framework that equips agents with communication strategies and environmental verification signals. Results show that aligned communication is crucial for rule understanding and human trust, while a verifier improves task comprehension and promotes safer, interpretable collaboration.
arXiv:2608. 13484v1 Announce Type: cross Abstract: When asked about entities outside their knowledge boundary, LLMs routinely fabricate plausible-sounding details rather than backing off to safer, more general claims.