Introduces Trajectory Proper Score (TPS) as a strictly proper family of trajectory-level scoring rules that elicits the complete prefix-conditioned success probability process.
Faith and fate: Limits of transformers on compositionality (2023).arXiv preprint arXiv:2305.18654
9 Pith papers cite this work. Polarity classification is still indexing.
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A transformer trained on random meaningless MicroPy programs generalizes to execute diverse human-written programs, providing empirical evidence it can act as a universal computer.
TSVer is a new benchmark dataset for fact verification against time-series evidence, with 304 annotated real-world claims, 400 time series, verdicts, and justifications, plus baseline results showing current models struggle.
A small GPT-2 model trained from scratch on GASING-derived CoT supervision for arithmetic reaches over 80% held-out accuracy, exhibits three learning phases, and develops both procedural and associative reasoning.
A decision-theoretic model based on the observed Confirmation-Diagnosis-Correction-Redo user pattern places intermediate confirmations in AI agent tasks, yielding 81% user preference and 13.54% faster completion versus confirm-at-end.
LLMs solve compositional factual recall either by computing intermediates or directly, with mechanism choice correlated to translation geometry in embedding spaces.
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.
Learnable graph patches enable domain-agnostic pre-training of graph models by decomposing heterogeneous graphs into transferable semantic units via patch encoders and aggregators.
LLM errors concentrate in sparse key tokens (5-10% of sequence) at semantic decision junctions, yielding a new reliability model that explains sustained long-context coherence.
citing papers explorer
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Proper Scoring Rules for Agentic Uncertainty Quantification
Introduces Trajectory Proper Score (TPS) as a strictly proper family of trajectory-level scoring rules that elicits the complete prefix-conditioned success probability process.
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Training Transformers as a Universal Computer
A transformer trained on random meaningless MicroPy programs generalizes to execute diverse human-written programs, providing empirical evidence it can act as a universal computer.
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TSVer: A Benchmark for Fact Verification Against Time-Series Evidence
TSVer is a new benchmark dataset for fact verification against time-series evidence, with 304 annotated real-world claims, 400 time series, verdicts, and justifications, plus baseline results showing current models struggle.
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Arithmetic Pedagogy for Language Models
A small GPT-2 model trained from scratch on GASING-derived CoT supervision for arithmetic reaches over 80% held-out accuracy, exhibits three learning phases, and develops both procedural and associative reasoning.
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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.
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Handling Feature Heterogeneity with Learnable Graph Patches
Learnable graph patches enable domain-agnostic pre-training of graph models by decomposing heterogeneous graphs into transferable semantic units via patch encoders and aggregators.
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Beyond Exponential Decay: Rethinking Error Accumulation in Large Language Models
LLM errors concentrate in sparse key tokens (5-10% of sequence) at semantic decision junctions, yielding a new reliability model that explains sustained long-context coherence.