E EULER is a multi-agent system that takes a bridge as its unit of search, and evaluates it on 120 recent conjectures drawn from public papers by authors who had recently published in the Journal of Combinatorial Theory, Series A.
Abstract
Mathematical communities work with different objects, invariants, and tools, so transferring a problem across them is expensive and often skipped. We present EULER, a multi-agent system that takes such a transfer--a bridge--as its unit of search. Around a fixed conjecture, EULER runs direct, adjacent-domain, and distant-domain routes in competition; a bridge keeps its budget only if it supplies an operation the source representation cannot execute and its target-side evidence returns to the original statement along a checked implication. Six ordered stress tests reject invalid bridges before expensive search begins. We evaluate EULER on 120 recent conjectures. The conjectures were frozen before search and screened for contamination, and are drawn from public papers by authors who had recently published in the Journal of Combinatorial Theory, Series A, a leading journal in combinatorics. EULER produced 10 proofs and 3 refutations, plus 45 scoped partial results. Two mechanisms held up under ablation: bridge-specific stress tests cut incorrect conclusions from 9 to 3, and bridge material combined with a target-native operation yielded a positive interaction of +4.2 resolved tasks that neither factor produced alone. Domain distance did not reliably predict success; executable operation gain and valid return did.
Stellar Colosseum is introduced, a model-agnostic harness for allocating inference across research in mathematics and theoretical computer science that demonstrates the capabilities of Colosseum through open-ended research and evaluations on theorem-proving and competitive programming benchmarks.
Hong-Hao Lin, David P. Woodruff, Yuan Deng et al.· 2 citations· ⚡1
COLD is an auditable measurement methodology, an evaluation contract that fixes a public information boundary, downstream stack G, and finite legal team family before outcomes are generated, which exposes selection headroom without manufacturing a routing win.
The results show that restricting peer reads during evidence gathering and strengthening commitment boundaries before a hypothesis is shared can broaden search and improve long-horizon multi-agent deep research.
Soyoung Yoon, Bo-Yi Liu, Yi-Te Wang et al.· 0 citations
The Station is evaluated, an open-world multi-agent environment in which AI agents from different model families pursue a shared research goal without a central coordinator or scripted pipeline, providing a transparent record of how discoveries emerged.
Stephen Chung, Wenyu Du, William J. Wesley· 2 citations
Agent skills are portable packages of instructions and resources an agent consults at deployment. Self-evolving them fails in two ways today. First, skills evolved from scratch underperform human-curated ones and, on a weak model, using no skill at all. Second, an evolution-time pass records one lucky trajectory that a...
Jia-Le Liu, Pin-Ze Ren, Yu Xia et al.· 0 citations
This work argues that robust evolution implies Structural Invariance: a reasoning path is valid only if its core dependency graph remains isomorphic under counterfactual perturbations, and enforces this with Verifiable Counterfactual Simulation (VCS).
Run-Ze Fan, Yong Li· Proceedings of the Thirty-Fi...· 0 citations
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.