The study examines how vision‑language models handle multi‑turn pragmatic interpretation in iterated reference games, where participants repeatedly identify novel referents using language. Researchers compared human performance with that of several models, manipulating context by varying its amount, order, and relevance. While humans consistently performed well, the models could use prior context but struggled to build relevant context for effective interpretation, indicating missing core skills for efficient linguistic collaboration.
arXiv:2601. 19792v4 Announce Type: replace-cross Abstract: For generative AI agents to partner effectively with human users, the ability to accurately predict human intent is critical.
CLUE is a two‑stage training pipeline that teaches models to discover and use social context cues within a conversation for pragmatic understanding. It combines supervised evidence reasoning with a GRPO reward that credits new, evidence‑supported social inferences while penalizing excessive reasoning effort. Experiments show that CLUE improves weighted F1 scores by 17.3% in‑domain and 9.3% out‑of‑domain, reduces reasoning tokens by over 80%, and aligns its explanations more closely with human‑selected conversational evidence.
arXiv:2609.12575v1 Announce Type: new Abstract: Ambiguity is often treated as a bug for AI systems to resolve---but in human communication and culture, ambiguity can also be a generative resource. Fr...
arXiv:2512. 06276v3 Announce Type: replace-cross Abstract: Referring Expression Comprehension (REC) is a vision-language task that localizes a specific image region based on a textual description.
Indirect speech acts (ISAs) require pragmatic reasoning over context, as directive intent can- not be inferred from surface form alone. Prior text-based studies and existing multimodal benchmarks larg...
The paper compares two approaches to pragmatic intent recognition in human‑robot interaction: FISER, which uses structured symbolic reasoning, and CEMIR, which enriches textual prompts with synthetic narratives and images. Experiments across four large language models on the HandMeThat v2 benchmark show no statistically significant performance difference between the two methods, regardless of model size or ambiguity difficulty. The results indicate that when task constraints are already present in the text, multimodal enrichment offers no measurable advantage over structured reasoning.
arXiv:2608.30270v1 Announce Type: new Abstract: Indirect speech acts (ISAs) require pragmatic reasoning over context, as directive intent can- not be inferred from surface form alone. Prior text-base...
arXiv:2609.14207v1 Announce Type: new Abstract: We propose to finetune vision-language models to generate more pragmatically optimal referring expressions by transforming observations of incremental...
arXiv:2610.19723v1 Announce Type: new Abstract: Interpreting an ambiguous utterance in real-world interactions often requires context beyond a single utterance. When the utterance refers to objects i...
For Your Eyes Only: Evaluating Coordination Between Isolated Language Model Instances explores whether a language model can embed a signal in natural language that another independent instance can detect without shared memory or coordination training. The study introduces a cooperative signalling game where a Sender describes two words, one hidden, and a Receiver must identify the target. Seven contemporary models from four architectural families were tested on 300 word pairs, revealing that most struggle to coordinate when signals must be undetectable, though one frontier model performs near-perfectly even after filtering, and that models can also use this capability for deliberate misdirection.
arXiv:2606. 31719v1 Announce Type: cross Abstract: In collaborative dialogue, shared perception does not guarantee shared interpretation.
The paper introduces a three‑layer checklist-and-judge framework to evaluate interpreter agents that mediate live conversation across languages. It assesses semantic, pragmatic, and cultural‑social dimensions—naturalness, intent, and social appropriateness—rather than just fidelity, in both single‑turn and multi‑turn settings. Extensive validation shows that conventional MT metrics miss failures in stronger interpreters, and that context, structured instructions, and cultural cues influence communicative success.