LinAlg-Bench shows LLMs switch from execution errors to computational abandonment and structured fabrication at 4x4 matrix scale, indicating a working memory limit rather than knowledge gaps.
Teaching arithmetic to small transformers
5 Pith papers cite this work. Polarity classification is still indexing.
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A new parameter reconstruction method achieves globally optimal training for spiking neural networks by convexifying parallel recurrent threshold networks that include SNNs as a special case.
FSLR explicitly supervises the initial logical planning step in math problems, boosting LLM accuracy by 3-5% while using 80% fewer training tokens than standard CoT fine-tuning.
FoNE encodes numbers as single tokens via Fourier features and outperforms subword and digit-wise embeddings on addition, subtraction, and multiplication with far less data.
LiveCodeBench collects 400 recent contest problems to create a contamination-free benchmark evaluating LLMs on code generation and related capabilities like self-repair and execution.
citing papers explorer
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LinAlg-Bench: A Forensic Benchmark Revealing Structural Failure Modes in LLM Mathematical Reasoning
LinAlg-Bench shows LLMs switch from execution errors to computational abandonment and structured fabrication at 4x4 matrix scale, indicating a working memory limit rather than knowledge gaps.
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Globally Optimal Training of Spiking Neural Networks via Parameter Reconstruction
A new parameter reconstruction method achieves globally optimal training for spiking neural networks by convexifying parallel recurrent threshold networks that include SNNs as a special case.
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From Implicit to Explicit: Token-Efficient Logical Supervision for Mathematical Reasoning in LLMs
FSLR explicitly supervises the initial logical planning step in math problems, boosting LLM accuracy by 3-5% while using 80% fewer training tokens than standard CoT fine-tuning.
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FoNE: Precise Single-Token Number Embeddings via Fourier Features
FoNE encodes numbers as single tokens via Fourier features and outperforms subword and digit-wise embeddings on addition, subtraction, and multiplication with far less data.
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LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code
LiveCodeBench collects 400 recent contest problems to create a contamination-free benchmark evaluating LLMs on code generation and related capabilities like self-repair and execution.