Static benchmarks often conflate memorization with reasoning, failing to capture the dynamic nature of world knowledge. We present LiveSearchBench, a scalable pipeline that automatically constructs snapshot-grounded benchmarks from knowledge graph differentials, in which every answer is unique and SPARQL-verifiable against a target snapshot. Unlike prior template-based approaches, our method leverages topology-aware synthesis to generate multi-constraint compositional questions with unique answers guaranteed via SPARQL validation. Evaluating 1,000 questions across three difficulty levels on both open-weight and frontier models, we expose a pronounced Recency Gap on facts that post-date a model’s training cutoff, particularly for compositional queries; retrieval dependence is thus an empirical property we measure, not a formal guarantee we claim. Through Oracle and Wikidata-as-corpus analyses, we disentangle dual failure modes: retrieval coverage limitations for single-hop questions and reasoning failures for compositional queries even with ground-truth evidence. LiveSearchBench provides both a rigorous construction methodology and a continuously updatable testbed for evaluating evidence integration under evolving knowledge.

Multi-agent LLM systems often fail not for lack of candidate answers, but because they have no persistent mechanism for deciding which incompatible claim should currently be trusted. Majority vote, debate, and judge-based selection choose an output without recording which claim wins, which is contested, or why a later update supersedes it. We present LatticeMind, a conflict-aware structured memory that handles contradiction at write time. It maintains explicit item status, applies cheap symbolic conflict checks, and routes the flagged cases to an LLM reconciler. On a label-blind ConflictBank evaluation that removes source-name hints, over a uniform draw of 200 examples from the full benchmark pool, LatticeMind reaches 0.965 accuracy versus 0.525 for the strongest baseline, with every gap significant at p<10^-24 by paired McNemar test. Ablations show that removing the checker or the reconciler costs 12 to 14 points. Our claims are scoped to credibility conflicts, where one claim should defeat another: the coordination branch of the design is a safety override that we do not validate empirically, and all prompt-based comparisons are conditioned on a single backbone. On three secondary planning benchmarks and one synthetic scheduling diagnostic the picture is mixed: LatticeMind beats naive merge on three of four, but does not replace deliberation methods on tasks rewarding iterative search.

While scaling laws for large language models (LLMs) during pre-training have been extensively studied, their behavior under reinforcement learning (RL) post-training remains largely unexplored. This paper investigates the scaling behavior of Large Language Model (LLM) reinforcement learning post-training, focusing on mathematical reasoning. Through experiments across the Qwen2.5 series (0.5B to 72B), we characterize how model scale, data, and compute interact. Our analysis yields four key findings: 1. Larger models consistently demonstrate superior compute and data efficiency. 2. The relationship between model performance and training resources follows a predictive power-law across both base and instruction-tuned models. 3. RL learning efficiency exhibits a latent saturation trend with increasing model scale. 4. In data-constrained regimes, performance is primarily driven by the total volume of training data rather than sample uniqueness. These results offer practical guidelines for scaling reasoning capabilities through reinforcement learning post-training.