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test-time-compute

topic3 events
papersTODAY 04:00 UTC

arXiv paper studies how evidence ordering shapes streaming test-time compute

A new arXiv paper argues that reasoning policies for a fixed task and compute budget should change depending on the order in which evidence arrives. It frames this as an "information-slack dilemma": computing early leaves more time to finish but relies on incomplete evidence, while waiting yields better information at the cost of fewer remaining steps. The authors contend that depth and width alone do not capture this tradeoff, which streaming settings must account for.

test-time-compute/reasoning/arxiv/llm
papersTODAY 04:00 UTC

Elo-per-token Analysis Measures Test-Time Scaling in LLM Agents

A new arXiv paper proposes an Elo-per-token method to assess how LLM agents spend test-time compute while revising answers, calling tools, exploring options, and deciding when to stop. Because agents allocate that compute adaptively, conventional measures struggle to capture how their performance scales, which the authors aim to address with their token-level rating approach.

agents/llm-agents/test-time-compute/benchmarks
papersSEP 12 04:00 UTC

arXiv paper examines looped models that update hidden states recurrently at inference

A new preprint discusses looped neural architectures that improve problem solving by repeatedly updating an internal hidden state during inference rather than scaling parameters. The authors note that training such models is challenging because backpropagation through repeated steps is costly. The work explores how these iteration-based approaches relate to spending more compute at test time.

research/arxiv/inference/recurrent-models