Humans exhibit abstraction learning consistent with prospective compression of future tasks in non-stationary domains, unlike retrospective compression algorithms or LLM-based approaches.
InThe Twelfth International Conference on Learning Representations (ICLR)
8 Pith papers cite this work, alongside 12 external citations. Polarity classification is still indexing.
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DEFT dynamically generates LLM-proposed biologically-informed features during decision tree construction to achieve interpretable and predictive DNA sequence classification.
RuleChef generates and refines executable rules for NLP tasks using LLMs only at learning time to produce fast, deterministic, and inspectable rule systems.
LLMs perform in-context learning as trajectories through a structured low-dimensional conceptual belief space, with the structure visible in both behavior and internal representations and causally manipulable via interventions.
Denoising Recursion Models train multi-step noise reversal in looped transformers and outperform the prior Tiny Recursion Model on ARC-AGI.
Cross-modal representational alignment between text, image, audio, and video models degrades substantially when evaluated at million-sample scale and under realistic many-to-many pairing assumptions.
An adaptive compute-optimal strategy for scaling LLM test-time compute achieves over 4x efficiency gains versus best-of-N and lets smaller models outperform 14x larger ones on some problems.
Quiet-STaR lets language models learn token-level rationales from general text, producing zero-shot gains on GSM8K and CommonsenseQA after continued pretraining.
citing papers explorer
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Prospective Compression in Human Abstraction Learning
Humans exhibit abstraction learning consistent with prospective compression of future tasks in non-stationary domains, unlike retrospective compression algorithms or LLM-based approaches.
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Interpretable DNA Sequence Classification via Dynamic Feature Generation in Decision Trees
DEFT dynamically generates LLM-proposed biologically-informed features during decision tree construction to achieve interpretable and predictive DNA sequence classification.
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RuleChef: Grounding LLM Task Knowledge in Human-Editable Rules
RuleChef generates and refines executable rules for NLP tasks using LLMs only at learning time to produce fast, deterministic, and inspectable rule systems.
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Stories in Space: In-Context Learning Trajectories in Conceptual Belief Space
LLMs perform in-context learning as trajectories through a structured low-dimensional conceptual belief space, with the structure visible in both behavior and internal representations and causally manipulable via interventions.
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One Step Forward and K Steps Back: Better Reasoning with Denoising Recursion Models
Denoising Recursion Models train multi-step noise reversal in looped transformers and outperform the prior Tiny Recursion Model on ARC-AGI.
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Back into Plato's Cave: Examining Cross-modal Representational Convergence at Scale
Cross-modal representational alignment between text, image, audio, and video models degrades substantially when evaluated at million-sample scale and under realistic many-to-many pairing assumptions.
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Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
An adaptive compute-optimal strategy for scaling LLM test-time compute achieves over 4x efficiency gains versus best-of-N and lets smaller models outperform 14x larger ones on some problems.
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Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking
Quiet-STaR lets language models learn token-level rationales from general text, producing zero-shot gains on GSM8K and CommonsenseQA after continued pretraining.