A^3-Bench: Benchmarking Memory-Driven Scientific Reasoning via Anchor and Attractor Activation
Scientific reasoning relies not only on logical inference but also on activating prior knowledge and experiential structures. Memory can efficiently reuse knowledge and enhance reasoning consistency and stability. However, existing benchmarks mainly evaluate final answers or step-by-step coherence, overlooking the memory-driven mechanisms that underlie human reasoning, which involves activating anchors and attractors, then integrating them into multi-step inference. To address this gap, we propose A^3-Bench~ https://a3-bench.github.io, a benchmark designed to evaluate scientific reasoning through dual-scale memory-driven activation, grounded in Anchor and Attractor Activation. First, we annotate 2,198 science reasoning problems across domains using the SAPM process(subject, anchor & attractor, problem, and memory developing). Second, we introduce a dual-scale memory evaluation framework utilizing anchors and attractors, along with the AAUI(Anchor--Attractor Utilization Index) metric to measure memory activation rates. Finally, through experiments with various base models and paradigms, we validate A^3-Bench and analyze how memory activation impacts reasoning performance, providing insights into memory-driven scientific reasoning.
6968568f0ac10a06522f69e9
Jian Zhang
claimed_verified
2026-02-05T10:57:29.771Z
658be7fe135580745c510323
/avatars/830e5cec4565efdc23226a86a0fcef0e.svg
Jian Zhang
VentureZJ
user
VentureZJ
6968568f0ac10a06522f69ea
Yu He
claimed_verified
2026-01-26T08:33:57.401Z
63006304a9df0b1a3285326f
/avatars/030f9aa81d1d6370a439e03514ef770f.svg
he
xinyu1607
user
xinyu1607
6968568f0ac10a06522f69eb
Zhiyuan Wang
claimed_verified
2026-01-15T15:03:02.764Z
67e0dc49daf1e39a7d15e67f
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/GsHxvMtp5jW58LunxVorc.png
Zhiyuan Wang
Pekku
user
Pekku
6968568f0ac10a06522f69ec
Zhangqi Wang
6968568f0ac10a06522f69ed
Kai He
6968568f0ac10a06522f69ee
Fangzhi Xu
claimed_verified
2026-02-06T18:57:10.600Z
64e6cf78ecce34cb442dc889
https://cdn-avatars.huggingface.co/v1/production/uploads/64e6cf78ecce34cb442dc889/qVZFiUEpBpSkmH8SQeinm.jpeg
Fangzhi Xu
xufangzhi
user
xufangzhi
6968568f0ac10a06522f69ef
Qika Lin
claimed_verified
2026-01-15T15:03:05.035Z
66ac77011cfb12c087605acb
/avatars/54c06bd1c4c9d491470ed4162c2301ae.svg
Lin
Qika
user
Qika
6968568f0ac10a06522f69f0
Jun Liu
A^3-Bench: Benchmarking Memory-Driven Scientific Reasoning via Anchor and Attractor Activation
Scientific reasoning relies not only on logical inference but also on activating prior knowledge and experiential structures. Memory can efficiently reuse knowledge and enhance reasoning consistency and stability. However, existing benchmarks mainly evaluate final answers or step-by-step coherence, overlooking the memory-driven mechanisms that underlie human reasoning, which involves activating anchors and attractors, then integrating them into multi-step inference. To address this gap, we propose A^3-Bench~ https://a3-bench.github.io, a benchmark designed to evaluate scientific reasoning through dual-scale memory-driven activation, grounded in Anchor and Attractor Activation. First, we annotate 2,198 science reasoning problems across domains using the SAPM process(subject, anchor & attractor, problem, and memory developing). Second, we introduce a dual-scale memory evaluation framework utilizing anchors and attractors, along with the AAUI(Anchor--Attractor Utilization Index) metric to measure memory activation rates. Finally, through experiments with various base models and paradigms, we validate A^3-Bench and analyze how memory activation impacts reasoning performance, providing insights into memory-driven scientific reasoning.