Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just
  Like an Olympiad Team
Despite impressive progress on complex reasoning, current large language
models (LLMs) typically operate in isolation - treating each problem as an
independent attempt, without accumulating or integrating experiential
knowledge. In contrast, expert problem solvers - such as Olympiad or
programming contest teams - leverage a rich tapestry of experiences: absorbing
mentorship from coaches, developing intuition from past problems, leveraging
knowledge of tool usage and library functionality, adapting strategies based on
the expertise and experiences of peers, continuously refining their reasoning
through trial and error, and learning from other related problems even during
competition. We introduce Xolver, a training-free multi-agent reasoning
framework that equips a black-box LLM with a persistent, evolving memory of
holistic experience. Xolver integrates diverse experience modalities, including
external and self-retrieval, tool use, collaborative interactions, agent-driven
evaluation, and iterative refinement. By learning from relevant strategies,
code fragments, and abstract reasoning patterns at inference time, Xolver
avoids generating solutions from scratch - marking a transition from isolated
inference toward experience-aware language agents. Built on both open-weight
and proprietary models, Xolver consistently outperforms specialized reasoning
agents. Even with lightweight backbones (e.g., QWQ-32B), it often surpasses
advanced models including Qwen3-235B, Gemini 2.5 Pro, o3, and o4-mini-high.
With o3-mini-high, it achieves new best results on GSM8K (98.1%), AIME'24
(94.4%), AIME'25 (93.7%), Math-500 (99.8%), and LiveCodeBench-V5 (91.6%) -
highlighting holistic experience learning as a key step toward generalist
agents capable of expert-level reasoning. Code and data are available at
https://kagnlp.github.io/xolver.github.io/.
68522d7f0164cd13167104ee
Md Tanzib Hosain
68522d7f0164cd13167104ef
Salman Rahman
68522d7f0164cd13167104f0
Md Kishor Morol
68522d7f0164cd13167104f1
Md Rizwan Parvez
Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just
  Like an Olympiad Team
Despite impressive progress on complex reasoning, current large language
models (LLMs) typically operate in isolation - treating each problem as an
independent attempt, without accumulating or integrating experiential
knowledge. In contrast, expert problem solvers - such as Olympiad or
programming contest teams - leverage a rich tapestry of experiences: absorbing
mentorship from coaches, developing intuition from past problems, leveraging
knowledge of tool usage and library functionality, adapting strategies based on
the expertise and experiences of peers, continuously refining their reasoning
through trial and error, and learning from other related problems even during
competition. We introduce Xolver, a training-free multi-agent reasoning
framework that equips a black-box LLM with a persistent, evolving memory of
holistic experience. Xolver integrates diverse experience modalities, including
external and self-retrieval, tool use, collaborative interactions, agent-driven
evaluation, and iterative refinement. By learning from relevant strategies,
code fragments, and abstract reasoning patterns at inference time, Xolver
avoids generating solutions from scratch - marking a transition from isolated
inference toward experience-aware language agents. Built on both open-weight
and proprietary models, Xolver consistently outperforms specialized reasoning
agents. Even with lightweight backbones (e.g., QWQ-32B), it often surpasses
advanced models including Qwen3-235B, Gemini 2.5 Pro, o3, and o4-mini-high.
With o3-mini-high, it achieves new best results on GSM8K (98.1%), AIME'24
(94.4%), AIME'25 (93.7%), Math-500 (99.8%), and LiveCodeBench-V5 (91.6%) -
highlighting holistic experience learning as a key step toward generalist
agents capable of expert-level reasoning. Code and data are available at
https://kagnlp.github.io/xolver.github.io/.
Xolver, a multi-agent reasoning framework, enhances large language models with persistent memory and diverse experience modalities, improving performance on complex reasoning tasks by avoiding generating solutions from scratch.
large language models LLMs multi-agent reasoning framework persistent memory experience-aware language agents external and self-retrieval tool use collaborative interactions agent-driven evaluation iterative refinement GSM8K AIME'24 AIME'25 Math-500 LiveCodeBench-V5 generalist agents expert-level reasoning