arXiv AI
Fitting Every Hint Is Not Enough: Premature Floods in Recurrent Algorithmic Reasoners
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arXiv AI
Oct 9
Scaling to Tens of Thousands of Test-Time Iterations with Loop-Native Attention Residuals
The paper introduces InfiLoop, a loop-native residual connection designed for looped Transformers to prevent performance degradation as iterations increase. InfiLoop learns which past computations to retain and how much to accept from each new update, combining content-based weighting with learned temporal decay to maintain a running summary of recurrent states. Across extensive reasoning tasks, a 7M-parameter InfiLoop model outperforms existing recursive architectures, achieving 97.9% exact accuracy on Sudoku-Extreme and 13.6% pass@2 on ARC-AGI-2, and continues to improve with test-time looping beyond 20,000 effective steps.
Hugging Face Trending Papers
Oct 8
Scaling to Tens of Thousands of Test-Time Iterations with Loop-Native Attention Residuals
The paper proposes InfiLoop, a loop-native residual connection designed for looped Transformers to prevent performance degradation as iterations increase. InfiLoop learns which past computations to retain and how much to accept from each new update, using content-based weighting and learned temporal decay to maintain a running summary of recurrent states. The approach yields strong results on reasoning tasks, achieving 97.9% exact accuracy on Sudoku-Extreme and 13.6% pass@2 on ARC-AGI-2, and continues to improve with over 20,000 test-time iterations.
arXiv Machine Learning
Jul 21
Taurus: Accelerating Out-of-Core Graph Neural Network Inference on Billion-Scale Graphs
arXiv:2607. 17374v1 Announce Type: cross Abstract: Graph Neural Network (GNN) inference on billion-scale graphs is challenging due to the large memory footprint of features and embeddings and high disk I/O costs in out-of-core settings.
arXiv Machine Learning
Oct 28
Resample the Answer: Where to Spend Test-Time Compute in Recursive Reasoning
The paper investigates how to allocate test‑time computation in recursive reasoning models, specifically the Tiny Recursive Model (TRM). Experiments show that resampling the answer state (y) is more effective than perturbing the reasoning state (z) for recovering failures, especially as compute budgets increase. The proposed Gated Answer Resampling (GAR) method, which restarts only rejected puzzles, boosts TRM performance from 85.9% to 99.4% on Sudoku‑Extreme and from 68.8% to 85.6% on the Pencil Puzzle Bench, outperforming the probabilistic variant that uses far more compute.
arXiv AI
Jul 7
Grokking Is Conditional and Fragile: A Fully-Tractable, Multi-Seed Study at 12K Parameters
arXiv:2607. 05104v1 Announce Type: cross Abstract: Grokking -- the delayed onset of generalization long after a network has fit its training set - -is usually studied in models too large to read completely and reported from single training runs.
arXiv AI
Sep 30
Beyond Compression: Diagnosing How Post-Training Changes Mathematical Reasoning
arXiv:2609.37066v1 Announce Type: cross Abstract: Post-training is central to mathematical reasoning in modern large language models (LLMs), but endpoint pass@1 alone underidentifies what has changed...