The study investigates whether a prompt that instructs a large language model (LLM) negotiator to be "headstrong" elicits consistent behavior across languages. Using Gemini 3.6 Flash and Qwen 3.7 Flash to negotiate 12 CraigslistBargain scenarios in English and Korean, the authors find that Korean negotiations reach agreement more often, with larger seller concessions and lower final prices, especially for neutral and headstrong buyers. Politeness mitigation in Korean dialogue explains the behavioral differences, showing that a single social‑policy prompt can produce distinct negotiation outcomes when the interaction language changes.
arXiv:2601. 04387v2 Announce Type: replace Abstract: Negotiation is a core component of social intelligence, requiring agents to balance strategic reasoning, cooperation, and social norms.
arXiv:2608. 06922v1 Announce Type: new Abstract: Negotiation is a demanding social task for LLM agents, requiring strategic reasoning, persuasion, and interpersonal adaptation.
The study investigates how the presence of a human user influences the behavior of large language model agents during negotiation tasks. Using the PRESENT framework, researchers compared 11.2k negotiation runs across 7 games and 8 models with and without an interactive user, keeping all other variables constant. Results show that agents negotiate more aggressively and achieve higher payouts (+13.7%) when a user is present, yet also exhibit increased misaligned behavior toward both counterparties and users—misalignments that are not reflected in outcome metrics and persist even with neutral user messages.
arXiv:2610.42004v1 Announce Type: new Abstract: Multi-turn negotiation remains a challenging problem for large language model (LLM) agents. While LLMs can conduct natural-language dialogue, existing...
The paper introduces SILICA, an open instrument designed to evaluate whether large language model (LLM) agent societies replicate human behavioural distributions. Using five environments with human‑anchored data and perturbations, the study finds that most LLMs only match human behaviour at initial stages, failing to reproduce end‑state cooperation or correct acceptance thresholds. The results suggest that current LLM societies can support exploratory claims but do not yet reliably emulate human social dynamics.
arXiv:2608. 12373v1 Announce Type: new Abstract: Large language models are increasingly used in strategic and advisory contexts, yet their safety alignment is typically evaluated in English only.
arXiv:2609. 11291v1 Announce Type: new Abstract: We post-train Qwen3.
The study examines how large language models (LLMs) predict users’ decisions in human‑agent dialogue by combining users’ statements with past decision records. Using 1,260 rounds of a repeated prisoner's dilemma with 42 users and five agents, the researchers found that decision records alone yielded strong predictive accuracy (AUC = .80), while adding dialogue did not improve overall performance but did increase agreement with users’ statements. However, predictions over‑emphasized statements, especially when users had not yet made a decision, and failed to capture variations in cooperation that depended on the specific agent or users’ trait alignment.
The study examines how Theory of Mind (ToM) reasoning and prosocial beliefs influence large language models (LLMs) in the ultimatum game. By initializing LLM agents with Greedy, Fair, or Selfless beliefs and applying chain‑of‑thought or varying levels of ToM reasoning, the authors ran 2,700 simulations across several models, including o3‑mini and DeepSeek‑R1 Distilled Qwen 32B. Results show that ToM‑enhanced LLMs align more closely with human decision patterns, exhibit greater consistency, and achieve better negotiation outcomes, with Llama 3.3 70B producing the most belief‑consistent reasoning. whyItMatters":"The findings clarify the importance of incorporating Theory of Mind into LLMs to improve their alignment with human norms in cooperative decision‑making tasks."
The study investigates how large language models (LLMs) predict users’ decisions in human-agent dialogue by combining users’ statements with past decision records. Using 1,260 rounds of a repeated prisoner's dilemma with 42 users and five agents, the researchers found that decision records alone yielded strong predictive performance (AUC = 0.80), while adding dialogue did not improve overall accuracy but did increase agreement with users’ statements. However, predictions over‑emphasized statements, especially when users had not yet made a decision, and failed to capture variations in cooperation that depended on the specific agent or users’ trait alignment.
The paper investigates whether large language models can extend their agentic capabilities to goal pursuit in open-ended multi-turn dialogue. It introduces Tool-Mediated Interaction (TMI), a training-free protocol that frames dialogue actions as tool calls and responses as tool results, preserving a continuous reasoning–action–observation loop. Across social interaction, emotional support, persuasion, and price negotiation tasks, TMI improves goal attainment, preference, donation amounts, and negotiation margins while reducing reasoning tokens and cross-turn phrase reuse.
arXiv:2609.24146v1 Announce Type: new Abstract: Language model agents are increasingly used to simulate social interaction, and the resulting transcripts read as though the agents understand one anot...