Moshi is the first real-time full-duplex spoken large language model that casts dialogue as speech-to-speech generation using parallel audio streams and an inner monologue of time-aligned text tokens.
Watermarking makes language models radioactive
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Characterizes the exact capacity of multi-bit covert LLM watermarking via Gelfand-Pinsker and channel synthesis, then gives a polar-code algorithm achieving 0.375 bits/token at under 10% BER with negligible perplexity impact.
Interaction-layer antidistillation watermarks use system-prompt-induced behavioral markers like explicit follow-up questions that transfer to distilled student models at 45-89% relative fidelity and can be audited via black-box LLM-as-judge queries.
The thesis presents a kernel method for multiaccuracy across overlooked subpopulations, information-theoretic optimal watermarking for LLMs, and a simulator showing LLM agents outperforming humans in supply chains while creating tail risks.
Watermark-based dataset inference achieves membership detection performance comparable to loss-based methods when subset exposure is high, under alternate assumptions.
citing papers explorer
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Moshi: a speech-text foundation model for real-time dialogue
Moshi is the first real-time full-duplex spoken large language model that casts dialogue as speech-to-speech generation using parallel audio streams and an inner monologue of time-aligned text tokens.
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Covert Multi-bit LLM Watermarking: An Information Theory and Coding Approach
Characterizes the exact capacity of multi-bit covert LLM watermarking via Gelfand-Pinsker and channel synthesis, then gives a polar-code algorithm achieving 0.375 bits/token at under 10% BER with negligible perplexity impact.
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Asking Back: Interaction-Layer Antidistillation Watermarks
Interaction-layer antidistillation watermarks use system-prompt-induced behavioral markers like explicit follow-up questions that transfer to distilled student models at 45-89% relative fidelity and can be audited via black-box LLM-as-judge queries.
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Trustworthy AI: Ensuring Reliability and Accountability from Models to Agents
The thesis presents a kernel method for multiaccuracy across overlooked subpopulations, information-theoretic optimal watermarking for LLMs, and a simulator showing LLM agents outperforming humans in supply chains while creating tail risks.
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Watermarking for Proprietary Dataset Protection
Watermark-based dataset inference achieves membership detection performance comparable to loss-based methods when subset exposure is high, under alternate assumptions.