Think-at-Hard selectively triggers latent iterations only on hard tokens via a neural decider and depth-aware LoRA, yielding 3.8-6.8% gains over baselines on nine reasoning benchmarks while iterating on just 7% of tokens.
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2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
Large-scale experiments on two million agents reveal that collective intelligence does not emerge from scale alone due to sparse and shallow interactions.
citing papers explorer
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Think-at-Hard: Selective Latent Iterations to Improve Reasoning Language Models
Think-at-Hard selectively triggers latent iterations only on hard tokens via a neural decider and depth-aware LoRA, yielding 3.8-6.8% gains over baselines on nine reasoning benchmarks while iterating on just 7% of tokens.
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Superminds Test: Actively Evaluating Collective Intelligence of Agent Society via Probing Agents
Large-scale experiments on two million agents reveal that collective intelligence does not emerge from scale alone due to sparse and shallow interactions.