Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T21:53:01.080614Z
Paper Citation Record · LEDGER
As of 19 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 0 inbound Pith citation observations for arXiv:2505.08878.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T21:53:01.080614Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
58 of 58 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation e58fe715-c82c-4751-8e9f-257a20de6b76 · outbound
Optimized Couplings for Watermarking Large Language Models TrustLLM: Trustworthiness in Large Language Models
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Optimized Couplings for Watermarking Large Language Models Constitutional AI: Harmlessness from AI Feedback
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Optimized Couplings for Watermarking Large Language Models Transformer feed-forward layers are key-value memories
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Optimized Couplings for Watermarking Large Language Models Truthfulqa: Measuring how models mimic human falsehoods
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Optimized Couplings for Watermarking Large Language Models A watermark for black-box language models
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Optimized Couplings for Watermarking Large Language Models Scalable watermarking for identifying large language model outputs
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Optimized Couplings for Watermarking Large Language Models Subtle signatures, strong shields: Advancing robust and imper- ceptible watermarking in large language models
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Optimized Couplings for Watermarking Large Language Models Unbiased watermark for large language models
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Optimized Couplings for Watermarking Large Language Models Permute-and-Flip: An optimally stable and watermarkable decoder for LLMs
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Observation e086c932-c0a0-4936-bbd7-7c5b92a48123 · outbound
Optimized Couplings for Watermarking Large Language Models Watermarking Language Models with Error Correcting Codes
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Observation f60daae3-1f91-4a74-b6ad-f8678e4fac0d · outbound
Optimized Couplings for Watermarking Large Language Models Provably Robust Multi-bit Watermarking for AI-generated Text
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Optimized Couplings for Watermarking Large Language Models Debiasing Watermarks for Large Language Models via Maximal Coupling
Reference 19
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Observation 3ba47ed3-5e5c-413e-98f5-b541ce5961d5 · outbound
Optimized Couplings for Watermarking Large Language Models Adaptive text watermark for large language models
Reference 20
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Optimized Couplings for Watermarking Large Language Models Three bricks to consolidate watermarks for large language models
Reference 21
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Optimized Couplings for Watermarking Large Language Models Coding for channels with random parameters
Reference 22
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Optimized Couplings for Watermarking Large Language Models An informationtheoretical approach to information embedding
Reference 23
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Optimized Couplings for Watermarking Large Language Models Design and analysis of digital watermarking, information embedding, and data hiding systems
Reference 24
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Optimized Couplings for Watermarking Large Language Models Information-theoretic analysis of information hiding
Reference 25
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Optimized Couplings for Watermarking Large Language Models Authentication with distortion criteria
Reference 26
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Optimized Couplings for Watermarking Large Language Models Text data-hiding for digital and printed documents: Theoretical and practical considerations
Reference 27
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Optimized Couplings for Watermarking Large Language Models Rethinking lossy compression: The rate-distortion-perception tradeoff
Reference 28
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Optimized Couplings for Watermarking Large Language Models A coding theorem for the rate-distortion-perception function
Reference 29
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Observation 31f98c93-3190-4e97-b7b6-21cc78105f50 · outbound
Optimized Couplings for Watermarking Large Language Models On the rate-distortion-perception function
Reference 30
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Optimized Couplings for Watermarking Large Language Models Information theory: From coding to learning
Reference 31
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Optimized Couplings for Watermarking Large Language Models WaterBench: Towards Holistic Evaluation of Watermarks for Large Language Models
Reference 32
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Observation a3d6164c-074a-49c5-bc75-0b0915443dc2 · outbound
Optimized Couplings for Watermarking Large Language Models Towards Optimal Statistical Watermarking
Reference 33
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Optimized Couplings for Watermarking Large Language Models SoK: Watermarking for AI-Generated Content
Reference 34
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Optimized Couplings for Watermarking Large Language Models Testing statistical hypotheses, volume 3
Reference 35
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Optimized Couplings for Watermarking Large Language Models Channel coding: Non-asymptotic fundamental limits
Reference 36
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Optimized Couplings for Watermarking Large Language Models Channel coding rate in the finite blocklength regime.IEEE Transactions on Information Theory, 56(5):2307–2359, 2010
Reference 37
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Optimized Couplings for Watermarking Large Language Models IEEE Transactions on Information Theory, 63(5):2629– 2658, 2016
Reference 38
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Optimized Couplings for Watermarking Large Language Models Contraction of $E_\gamma$-Divergence and Its Applications to Privacy
Reference 39
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Optimized Couplings for Watermarking Large Language Models Information Theory: From Coding to Learning
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Optimized Couplings for Watermarking Large Language Models Finite exchangeable sequences
Reference 42
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Optimized Couplings for Watermarking Large Language Models The Llama 3 Herd of Models
Reference 43
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Optimized Couplings for Watermarking Large Language Models Gurobi Optimizer Reference Manual, 2024
Reference 44
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Optimized Couplings for Watermarking Large Language Models On the Reliability of Watermarks for Large Language Models
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Optimized Couplings for Watermarking Large Language Models A Statistical Framework of Watermarks for Large Language Models: Pivot, Detection Efficiency and Optimal Rules
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Optimized Couplings for Watermarking Large Language Models MarkLLM: An Open-Source Toolkit for LLM Watermarking
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Optimized Couplings for Watermarking Large Language Models Evaluating durability: Benchmark insights into image and text watermarking
Reference 49
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Optimized Couplings for Watermarking Large Language Models A survey of text watermarking in the era of large language models
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Optimized Couplings for Watermarking Large Language Models In this case, there is at least one possible value for eachqj that results in a nonnegative value off(qj)
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Reference 52
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Reference 53
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Optimized Couplings for Watermarking Large Language Models To show that this is a valid coupling, we verify the marginal distribution is kept the same
Reference 54
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Optimized Couplings for Watermarking Large Language Models Hence,H0 is rejected by CC detection test
Reference 55
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Reference 56
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Reference 57
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Optimized Couplings for Watermarking Large Language Models 1− s PS|Y (s|Y (X,bm)) P (s) # , whereP (S|Y ) is the correlated channel. AssumingS∼ Ber 1 2 , we have H2 PQ,P eQ = 2ES,X
Reference 58
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No inbound Pith citation observations are available.