Proof pending. Core topic summary fields are still materializing.
Dialogue systems are evolving to better handle complex interactions by addressing challenges such as data inconsistency, context awareness, and user behavior diversity. Recent advancements include frameworks for selecting high-quality multi-turn dialogues, context-aware turn-taking in multi-party settings, and user simulators that mimic natural speech patterns. These innovations are crucial for enhancing the performance of task-oriented dialogue agents, ensuring they can manage non-linear conversations and maintain coherence over extended interactions. By improving the underlying models and datasets, builders can create more effective and user-friendly dialogue systems that better understand and respond to human communication nuances, ultimately leading to more engaging and productive user experiences.
Instruction-tuned language models increasingly rely on large multi-turn dialogue corpora, but these datasets are often noisy and structurally inconsistent, with topic drift, repetitive chitchat, and m...
Existing voice AI assistants treat every detected pause as an invitation to speak. This works in dyadic dialogue, but in multi-party settings, where an AI assistant participates alongside multiple spe...
Robust task-oriented spoken dialogue agents require exposure to the full diversity of how people interact through speech. Building spoken user simulators that address this requires large-scale spoken ...
Large Language Models demonstrate outstanding performance in many language tasks but still face fundamental challenges in managing the non-linear flow of human conversation. The prevalent approach of ...
Large language model (LLM) agents often exhibit abrupt shifts in tone and persona during extended interaction, reflecting the absence of explicit temporal structure governing agent-level state. While ...
Existing dynamic Theory of Mind (ToM) benchmarks mostly place language models in a passive role: the model reads a sequence of connected scenarios and reports what people believe, feel, intend, and do...
This paper studies how empirical dialogue-flow statistics can be incorporated into Next Dialogue Act Prediction (NDAP). A KL regularization term is proposed that aligns predicted act distributions wit...
The reasoning capability of large language models (LLMs), defined as their ability to analyze, infer, and make decisions based on input information, is essential for building intelligent task-oriented...
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Canonical route: /topics
Agent Handoff
Canonical ID dialogue-systems | Route /topic/dialogue-systems
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/topic/dialogue-systemsMCP example
{
"tool": "search_papers",
"arguments": {
"query": "Dialogue Systems",
"cluster": "Dialogue Systems"
}
}source_context
{
"surface": "topic",
"mode": "topic",
"query": "Dialogue Systems",
"normalized_query": "dialogue-systems",
"route": "/topic/dialogue-systems",
"paper_ref": null,
"topic_slug": "dialogue-systems",
"benchmark_ref": null,
"dataset_ref": null
}Use This Via API or MCP
Topic pages bundle paper counts, viability trends, author concentration, and top questions into one canonical surface your agents can reference before they open Signal Canvas or create a workspace.