Prototypical signatures enable generation of diverse negative samples for writer-independent offline signature verification, improving skilled forgery detection and allowing scalable linear SVM alternatives to RBF models.
Trgp: Trust region gradient projection for continual learning
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
years
2026 5verdicts
UNVERDICTED 5representative citing papers
TILR identifies low-rank invariant subspaces from contrastive latent trajectory differences in LLMs and constrains interventions to them, improving paraphrase consistency by ~10% and reducing variance by up to 50%.
The paper reformulates industrial continual learning for LLMs as a closed-loop ecosystem problem, identifies three core challenges, and organizes solutions around five lifecycle design principles.
FINCH is a loss-adaptive learning-rate schedule that reduces forgetting by 93% on average during LLM fine-tuning while matching standard task performance across several benchmarks.
C-Flat Turbo accelerates continual learning by skipping redundant flatness gradients via direction-invariance observations and linear adaptive scheduling, delivering 1-1.25x speedup with comparable accuracy.
citing papers explorer
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A Prototypical Signature Approach for Writer-Independent Offline Signature Verification
Prototypical signatures enable generation of diverse negative samples for writer-independent offline signature verification, improving skilled forgery detection and allowing scalable linear SVM alternatives to RBF models.
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Invariant Reasoning Directions in Latent Trajectories of Language Models
TILR identifies low-rank invariant subspaces from contrastive latent trajectory differences in LLMs and constrains interventions to them, improving paraphrase consistency by ~10% and reducing variance by up to 50%.
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LLM Evolution as an Industry-Scale Ecosystem: A Lifecycle Perspective on Continual Learning
The paper reformulates industrial continual learning for LLMs as a closed-loop ecosystem problem, identifies three core challenges, and organizes solutions around five lifecycle design principles.
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Fine-Tuning Without Forgetting via Loss-Adaptive Learning Rates
FINCH is a loss-adaptive learning-rate schedule that reduces forgetting by 93% on average during LLM fine-tuning while matching standard task performance across several benchmarks.
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A Faster Path to Continual Learning
C-Flat Turbo accelerates continual learning by skipping redundant flatness gradients via direction-invariance observations and linear adaptive scheduling, delivering 1-1.25x speedup with comparable accuracy.