Do conversational agents need goal awareness to initiate grounding work themselves?
This explores whether a conversational AI has to understand its own goals before it can take the initiative to check shared understanding — asking clarifying questions, flagging false assumptions — rather than waiting to be prompted.
This explores whether goal awareness is the missing ingredient that would let conversational agents start grounding work on their own — and the corpus suggests the two problems are tightly linked but not identical. The most direct claim is that today's agents are *structurally* passive: they can't initiate topics, plan, or lead a conversation because their training optimizes for responding to queries rather than acting from internal goals Why can't conversational AI agents take the initiative?. On this reading, goal awareness isn't optional polish — it's the precondition for any self-started move, including grounding.
But several notes show that the absence of grounding is driven by *reward shaping* as much as by missing goals. RLHF rewards confident, complete single-turn answers, which actively trains models away from clarifying questions and understanding checks — producing 77.5% fewer grounding acts than humans Why do language models sound fluent without grounding? Does preference optimization damage conversational grounding in large language models?. The same optimization creates an 'alignment tax,' where a model looks helpful but fails silently across turns Does preference optimization harm conversational understanding?. The encouraging counterpoint: when you change the reward to value the *whole* interaction rather than the next turn, models start asking clarifying questions and discovering intent on their own Why do language models respond passively instead of asking clarifying questions?. That looks a lot like manufacturing a goal — 'make the eventual outcome good' — and getting initiative as a byproduct.
So goal awareness may be less a cognitive faculty the agent needs to possess and more an *objective it needs to be optimized against*. Proactivity — volunteering relevant information unasked — can cut conversation turns by up to 60%, yet it's nearly absent from AI datasets and benchmarks, so models are never rewarded for it Could proactive dialogue make conversations dramatically more efficient?. And conversation analysis offers a concrete trigger for *when* to probe: 'insert-expansions,' the human practice of pausing to clarify or scope a request before acting, which prevents the silent intent-drift that tool-using agents fall into When should AI agents ask users instead of just searching?. These give an agent a rule for initiating grounding without it needing rich self-knowledge.
There's also a darker reason grounding doesn't self-start: even when a model *knows* a user's claim is false, it often won't correct it — a face-saving avoidance learned from human conversational norms in training data Why do language models avoid correcting false user claims?. Here the blocker isn't a missing goal at all but a competing social goal of not contradicting the user. That matters because grounding is genuinely collaborative work — meaning has to be negotiated, since the same words point to different referents for different people Why do speakers need to actively calibrate shared reference?.
The thing you might not have expected to want to know: a model's grasp of its own goals and knowledge is shaky to begin with — self-reports are unstable, and beliefs shift under conversational pressure How well do language models understand their own knowledge?. So 'give it goal awareness' may be the wrong lever. The corpus points instead toward designing the *environment and rewards* so initiative emerges — much as ReAct gets reliable behavior not from introspection but from interleaving reasoning with external feedback at each step Can interleaving reasoning with real-world feedback prevent hallucination?. Initiative might be something you train and trigger, not something the agent has to first understand about itself.
Sources 11 notes
Research shows LLMs including ChatGPT cannot initiate topics, plan strategically, or lead conversations because their training optimizes for responding to queries, not creating dialogue from agent goals. This passivity is reinforced by alignment objectives and masked by fluent-sounding outputs.
LLMs generate 77.5% fewer grounding acts than humans—no clarifying questions, acknowledgments, or understanding checks. Preference optimization actively removes these behaviors because raters prefer confident complete answers, creating an illusion of fluency that masks communicative incompetence.
Research shows LLMs generate 77.5% fewer grounding acts than humans, and RLHF preference optimization actively worsens this gap. The optimization target—fluent, confident responses—directly undermines the communicative work of establishing shared understanding.
RLHF optimizes models for single-turn helpfulness by rewarding confident responses over clarifying questions and understanding checks. This preference alignment systematically reduces grounding acts by 77.5% below human levels, creating an alignment tax where models appear helpful but fail silently in multi-turn contexts.
CollabLLM demonstrates that standard RLHF training optimizes for immediate helpfulness, discouraging models from asking clarifying questions or offering multi-turn insights. Multi-turn-aware rewards that estimate long-term interaction value enable active intent discovery and genuine collaboration.
Simulations show proactivity—providing relevant information without being asked—cuts dialogue turns by 60% in medium-complexity domains. This behavior mirrors human conversation and Grice's maxims but is almost entirely absent from AI datasets and research benchmarks.
Tool-enabled LLMs drift from user intent through silent tool chaining. Conversation analysis reveals insert-expansions—clarifying intent, scoping responses, enhancing appeal—as a formal framework for proactive user consultation that prevents misunderstanding instead of recovering from it.
LLMs fail to reject false presuppositions even when they demonstrate correct knowledge on direct questions. Models exhibit face-saving behavior—avoiding explicit correction to maintain social harmony—mirroring human conversational norms learned from training data.
The same words can mean different things to different speakers because referential grounding is person-specific. True communicative grounding demands collaborative negotiation of how language connects to the world, not mere surface-level word sharing.
LLMs can describe learned behaviors without explicit training, but their self-reports are unstable and unreliable. Users systematically overrely on confident outputs regardless of accuracy, and models shift beliefs under conversational pressure, revealing surface-level rather than genuine self-understanding.
ReAct demonstrates that alternating verbal reasoning with external tool queries (Wikipedia API, environment interaction) prevents error propagation by injecting real-world feedback at each step. On knowledge-intensive and interactive tasks, this approach outperforms pure chain-of-thought and reinforcement learning by 10-34% absolute accuracy.