Adaptive scheduling of interventions in discrete diffusion language models, timed to attribute-specific commitment schedules discovered with sparse autoencoders, delivers precise multi-attribute steering up to 93% strength while preserving generation quality.
Patel, Parth Sheth, et al
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Fine-tuning LLMs on an unseen language teaches syntax but fails to transfer semantic competence, leaving Python with up to a 19% performance advantage and no tested intervention closing the gap.
Transformer hidden states contain rank-indexed orientation signatures for true r-argument relations (r=3-6) that survive surface controls and can be patched to alter model outputs on relation tasks.
AI deployment in high-stakes areas requires domain-scoped calibrated verification with monitoring and revocation, using a proposed six-component Verification Coverage standard instead of mechanistic interpretability.
Overthinking in medical QA is linearly decodable at 71.6% accuracy yet fixed residual-stream steering yields no correction across 29 configurations, while enabling selective abstention with AUROC 0.610.
citing papers explorer
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Steering Without Breaking: Mechanistically Informed Interventions for Discrete Diffusion Language Models
Adaptive scheduling of interventions in discrete diffusion language models, timed to attribute-specific commitment schedules discovered with sparse autoencoders, delivers precise multi-attribute steering up to 93% strength while preserving generation quality.
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Syntax Without Semantics: Teaching Large Language Models to Code in an Unseen Language
Fine-tuning LLMs on an unseen language teaches syntax but fails to transfer semantic competence, leaving Python with up to a 19% performance advantage and no tested intervention closing the gap.
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Relational Rank Geometry in Transformers: Detecting and Steering Hidden-State Relation Frames
Transformer hidden states contain rank-indexed orientation signatures for true r-argument relations (r=3-6) that survive surface controls and can be patched to alter model outputs on relation tasks.
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The Open-Box Fallacy: Why AI Deployment Needs a Calibrated Verification Regime
AI deployment in high-stakes areas requires domain-scoped calibrated verification with monitoring and revocation, using a proposed six-component Verification Coverage standard instead of mechanistic interpretability.
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Decodable but Not Corrected by Fixed Residual-Stream Linear Steering: Evidence from Medical LLM Failure Regimes
Overthinking in medical QA is linearly decodable at 71.6% accuracy yet fixed residual-stream steering yields no correction across 29 configurations, while enabling selective abstention with AUROC 0.610.