Conditional Co-Ablation recovers self-repair backup heads in transformers by scoring conditional ablation growth, raising ROC-AUC from 0.33 to 0.91 on the IOI circuit and transferring to induction across models.
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4 Pith papers cite this work. Polarity classification is still indexing.
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A parameter-neutral fuzzy-logic FFN augmented with self-forgetting quantifiers produces legible grammatical-licensing detectors while matching baseline perplexity on OpenWebText.
Coverage-aware pruning using per-corpus utility profiles on WikiText2 and C4 improves zero-shot accuracy and reduces perplexity degradation in two MoE models at 25-75% retention compared to baselines, without downstream data.
LuckyStar 111B adapts Cohere's Command A model with four scaling techniques to improve tool-use, math reasoning, and NL2SQL in Korean-English while preserving general instruction following.
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Conditional Co-Ablation: Recovering Self-Repair Backups in Transformer Circuits
Conditional Co-Ablation recovers self-repair backup heads in transformers by scoring conditional ablation growth, raising ROC-AUC from 0.33 to 0.91 on the IOI circuit and transferring to induction across models.
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Explicit Fuzzy Logic in the Feed-Forward Layer: Self-Forgetting Quantifiers Discover Legible Grammatical-Licensing Detectors
A parameter-neutral fuzzy-logic FFN augmented with self-forgetting quantifiers produces legible grammatical-licensing detectors while matching baseline perplexity on OpenWebText.
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Generic Expert Coverage for Pruning SparseMixture-of-Experts Language Models
Coverage-aware pruning using per-corpus utility profiles on WikiText2 and C4 improves zero-shot accuracy and reduces perplexity degradation in two MoE models at 25-75% retention compared to baselines, without downstream data.
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Think in English, Answer in Korean: Efficient Adaptation of Multilingual Tool-Using Agents
LuckyStar 111B adapts Cohere's Command A model with four scaling techniques to improve tool-use, math reasoning, and NL2SQL in Korean-English while preserving general instruction following.