Beam Memory Benchmark: Key Findings On 1M Context

BEAM, introduced in the ICLR 2026 paper “Beyond a Million Tokens,” is a benchmark intended to assess whether large language models can maintain long-term memory across coherent, evolving conversations rather than simply retrieve details from large context windows. It addresses limitations of earlier long-context benchmarks, which often rely on disconnected sessions, narrow domains, and recall-oriented questions that may be solved through local retrieval. BEAM generates 100 narrative-driven conversations of up to 10 million tokens, containing persistent identities, changing facts, contradictions, preferences, timelines, and follow-up exchanges, and evaluates 10 memory-related capabilities through 2,000 probing questions. In comparisons among full-context models, retrieval-augmented generation baselines, and the proposed LIGHT structured-memory system, the paper reports that structured memory performs better as conversations become longer, particularly for summarization, multi-hop reasoning, and preference tracking, while contradiction resolution remains difficult for all systems. Its nugget-based scoring method measures partially correct answers more granularly than binary evaluation, though the account notes potential ambiguity for open-ended tasks such as summarization and concerns about unspecified judging models and prompts.

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