TiCo enables spoken dialogue models to follow explicit time constraints in generated responses using Spoken Time Markers and reinforcement learning with verifiable rewards, cutting duration error by 2.7x over its backbone.
Stream rag: Instant and accurate spoken dialogue systems with streaming tool usage,
8 Pith papers cite this work. Polarity classification is still indexing.
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ProactiveLLM enables active interaction in streaming LLMs by learning semantic sufficiency cues from partial inputs through mask-based modeling and synchronized privileged self-distillation without external supervision.
RADAR defends RAG systems in dynamic settings by framing reliable context selection as a Max-Flow Min-Cut graph problem with Bayesian memory updates, claiming superior robustness, response quality, and low storage on a new dynamic dataset.
Speculative Interaction Agents achieve 1.3-2.2x speedups for real-time tool-calling agents via async I/O decoupling and speculative calls, with clock-based training for small edge models.
VoxMind equips spoken dialogue models with agentic tool-use via a curated dataset, Think-before-Speak reasoning, and multi-agent tool management, raising task completion from 34.88% to 74.57% while preserving conversational quality.
A speech-based model forecasts conversation turn endpoints up to 2.56 seconds ahead to enable lower-latency spoken dialogue via speculative LLM and TTS execution.
PlanRAG-Audio introduces planning-based retrieval-augmented generation to improve accuracy and stability of long-form audio understanding in LALMs by decoupling model input from raw audio duration.
A survey that provides a unified formulation of audio reasoning and reviews advances across Audio-to-Text, Audio-to-Speech, Audio-Visual, and Agentic paradigms while discussing challenges and future directions.
citing papers explorer
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TiCo: Time-Controllable Spoken Dialogue Model
TiCo enables spoken dialogue models to follow explicit time constraints in generated responses using Spoken Time Markers and reinforcement learning with verifiable rewards, cutting duration error by 2.7x over its backbone.
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ProactiveLLM: Learning Active Interaction for Streaming Large Language Models
ProactiveLLM enables active interaction in streaming LLMs by learning semantic sufficiency cues from partial inputs through mask-based modeling and synchronized privileged self-distillation without external supervision.
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RADAR: Defending RAG Dynamically against Retrieval Corruption
RADAR defends RAG systems in dynamic settings by framing reliable context selection as a Max-Flow Min-Cut graph problem with Bayesian memory updates, claiming superior robustness, response quality, and low storage on a new dynamic dataset.
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Speculative Interaction Agents: Building Real-Time Agents with Asynchronous I/O and Speculative Tool Calling
Speculative Interaction Agents achieve 1.3-2.2x speedups for real-time tool-calling agents via async I/O decoupling and speculative calls, with clock-based training for small edge models.
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VoxMind: An End-to-End Agentic Spoken Dialogue System
VoxMind equips spoken dialogue models with agentic tool-use via a curated dataset, Think-before-Speak reasoning, and multi-agent tool management, raising task completion from 34.88% to 74.57% while preserving conversational quality.
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Endpoint Anticipation for Low-Latency Spoken Dialogue
A speech-based model forecasts conversation turn endpoints up to 2.56 seconds ahead to enable lower-latency spoken dialogue via speculative LLM and TTS execution.
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PlanRAG-Audio: Planning and Retrieval Augmented Generation for Long-form Audio Understanding
PlanRAG-Audio introduces planning-based retrieval-augmented generation to improve accuracy and stability of long-form audio understanding in LALMs by decoupling model input from raw audio duration.
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A Survey of Audio Reasoning in Multimodal Foundation Models
A survey that provides a unified formulation of audio reasoning and reviews advances across Audio-to-Text, Audio-to-Speech, Audio-Visual, and Agentic paradigms while discussing challenges and future directions.