VideoFDB is a new benchmark and LM-as-judge framework for evaluating full-duplex audio-visual-to-audio-visual conversational agents on nonverbal dynamics from real video calls.
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In2024 IEEE Spo- ken Language Technology Workshop (SLT), pages 1115–1122
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FD-SLMs exhibit state inertia during abrupt interruptions that a training-free perception-vector steering intervention mitigates, lifting correctness from 28% to 45% and IWOR from 40% to 72% on the Zero-Buffer Benchmark.
SpeechEditBench provides seven atomic editing tasks, compositional multi-operation instructions, and an anchor-based protocol yielding target success, preservation success, and joint success metrics; evaluations show no model excels across dimensions and compositional editing is especially difficult
DuplexSLA introduces a three-channel full-duplex architecture that synchronizes continuous user audio, discrete assistant audio, and rate-limited textual actions inside a single backbone for native turn-taking and in-conversation tool use.
EVA-Bench supplies a simulation engine for bot-to-bot voice dialogues plus two composite metrics (EVA-A for accuracy, EVA-X for experience) evaluated on 213 enterprise scenarios, showing no tested system exceeds 0.5 on both pass@1 scores.
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.
Game-Time Benchmark shows spoken language models handle basic tasks but degrade sharply under temporal constraints like tempo adherence and synchronized responses.
FacePlex introduces a unified streaming model with Rolling Flow Matching and Rolling Cross-Attention to enable full-duplex joint real-time generation of speech and facial motion tokens.
A multi-axis RL alignment technique improves pause handling, turn-taking, backchanneling, and interruption response in full-duplex spoken dialogue models by optimizing axis-specific rewards derived from human audio segments.
Engagement Process (EP) decouples actions and observations as independent event streams over time within a POMDP structure to explicitly model temporal dynamics in agent interactions.
ASPIRin decouples speaking timing from token content via binary action space projection and applies GRPO with rule-based rewards to optimize interactivity in SLMs without semantic collapse or repetition.
MM-tau-p² is a new benchmark with 12 metrics that measures how well multi-modal agents adapt to user personas and maintain robustness in dual-control interactions.
The survey introduces a four-category taxonomy for LALM evaluations and reviews benchmarks across general auditory processing, knowledge reasoning, dialogue, and fairness-safety.
Full-duplex SDMs show strong representational synchronization that peaks near zero lag and degrades with noise, with internal states encoding anticipatory turn-taking cues detectable ahead of time.
A survey of Large Audio Language Models that establishes a taxonomy of trustworthiness vulnerabilities and proposes a Defense-in-Depth roadmap for audio intelligence.
A new HumDial-FDBench benchmark and real human-recorded dual-channel dataset are released to assess full-duplex dialogue systems on interruptions and conversational flow.
A roadmap that defines architectural nativity for multimodal models and categorizes them into Multi-to-Text, Multi-to-Target, and Multi-to-Multi types while outlining an industrial pipeline toward unified transformer-based native multimodal modeling.
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Engagement Process: Rethinking the Temporal Interface of Action and Observation
Engagement Process (EP) decouples actions and observations as independent event streams over time within a POMDP structure to explicitly model temporal dynamics in agent interactions.