Budget Boundary Effects in Test-Time Mathematical Reasoning
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
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The paper investigates whether large language models’ reasoning traces truly contain early, informative signals or merely reflect budget and difficulty confounds. Using a restart‑controlled truncation probe, the authors compare continuation success rates against from‑scratch restart curves across 178 problem‑model pairs, finding that only one case shows prefix‑limited success and that continuing a model’s own prefix generally outperforms restarting. A difficulty‑controlled test and two generation‑free analyses reveal that early internal signals do not carry outcome information beyond a problem‑difficulty baseline, underscoring the need for proper counterfactual controls.
arXiv:2608. 04160v1 Announce Type: cross Abstract: Multilingual evaluations report accuracy at a single output-token cap, but languages need different numbers of tokens to express the same content, so the cap is a hidden experimental variable.
arXiv:2608. 03401v1 Announce Type: cross Abstract: Large language models often reason at length before answering, increasing cost and latency.
The paper investigates whether providing candidate solutions during test‑time aggregation improves or harms accuracy compared to a fresh solve that does not use any candidates. Using Qwen3‑4B on AIME‑2025 and HMMT‑2025, the authors find that conditioning on multiple correct candidates boosts accuracy (+0.290), while conditioning on an all‑wrong candidate pool reduces accuracy (−0.123); the effect for a single correct candidate remains unclear. The study also explores structured interventions and placebo controls, but the underlying mechanisms of these effects are not resolved.
arXiv:2608. 07303v1 Announce Type: new Abstract: Comparisons between AutoML systems at short time budgets -- tens of seconds rather than hours -- are common in tool READMEs and workshop papers, and they are easy to get wrong.
arXiv:2606. 19808v1 Announce Type: new Abstract: Test-time reasoning is increasingly used as a serving-time control knob, but extra reasoning is not uniformly valuable: it can repair failed attempts, waste compute on already-correct answers, or introduce harmful answer changes.