Large Language Models (LLMs) have enabled Multi-Agent Systems (MASs) where agents interact through natural language to solve complex tasks or simulate multi-party dialogues. Recent work on LLM-based MASs has mainly focused on architecture design, such as role assignment and workflow orchestration. In contrast, this paper targets the interaction process itself, aiming to improve agents’ communication efficiency by helping them convey their intended meaning more effectively through language. To this end, we propose LinguaGame, a linguistically-grounded game-theoretic paradigm for multi-agent dialogue generation. Our approach models dialogue as a signalling game over communicative intents and strategies, solved with a training-free equilibrium approximation algorithm for inference-time decision adjustment. Unlike prior game-theoretic MASs, whose game designs are often tightly coupled with task-specific objectives, our framework relies on linguistically informed reasoning with minimal task-specific coupling. Specifically, it treats dialogue as intentional and strategic communication, requiring agents to infer what others aim to achieve (intents) and how they pursue those goals (strategies). We evaluate our framework in simulated courtroom proceedings and debates, with human expert assessments showing significant gains in communication efficiency. We release our code and data on GitHub.

While LLMs demonstrate remarkable reasoning capabilities and multi-agent applicability, their tendency to “overthink” and “groupthink” pose intriguing parallels to human cognitive limitations. Inspired by this observation, we conduct an exploratory simulation to investigate whether LLMs are wise enough to be thinkers of philosophical reflection. We design two frameworks, Philosopher and Symposium, which simulate self- and group-reflection for multi-persona in hybrid table reasoning tasks. Through experiments across four benchmarks, we discover that while introducing varied perspectives might help, LLMs tend to under-perform simpler end-to-end approaches. We reveal from close reading five emergent behaviors which strikingly resemble human cognitive closure-seeking behaviors, and identify a consistent pattern of “overthinking threshold” across all tasks, where collaborative reasoning often reaches a critical point of diminishing returns. This study sheds light on a fundamental challenge shared by both human and machine intelligence: the delicate balance between deliberation and decisiveness.

Puns, as a unique form of linguistic creativity, present significant challenges in cross-lingual translation, particularly between linguistically distant languages like Chinese and English, where it’s often considered a “mission impossible”. We introduce Pun2Pun, a novel benchmark for quantitatively evaluating pun translation between Chinese and English while preserving both linguistic mechanisms and humorous effects. We propose the adaptation of Constant-Variable Optimization (CVO) Model for translation strategy and concomitant Overlap (Ovl) metric for translation quality assessment. Our approach provides a robust quantitative evaluation framework to assess models’ complex linguistic and cultural reasoning capabilities in pun translation. Through extensive experiments on both textual and visual puns, we demonstrate that our translation strategy model significantly improves performance, particularly for better-performing models. Our findings reveal exciting potentials and current limitations of LLMs in preserving sophisticated humor across linguistic and cultural boundaries.