Proximity trap limits long-context LLM performance more than distance
Researchers identify a fundamental failure mode in long-context LLMs: models struggle with distant evidence not because of distance itself, but because nearby irrelevant text drowns out task-critical signals. The team proposes LYRA, a retrieval mechanism using heavy-tailed distributions to amplify attention toward relevant passages while preserving positional structure. Validated across LongBench-v2, RULER, and LongBench, this work reframes a core limitation of scaling context windows. For practitioners building retrieval-augmented systems, the finding suggests that context length alone is insufficient without architectural fixes to handle noise accumulation.
Modelwire context
ExplainerThe paper's core insight inverts the problem statement: the issue isn't that models can't reach distant tokens, but that intervening noise actively suppresses relevant signals. This reframes context scaling as an architectural problem, not merely a capacity problem.
This connects directly to CliffCompaction (arXiv cs.LG, 2026-09-22), which tackles context window limits through compression. Where CliffCompaction solves the cost side of long-horizon reasoning, LYRA addresses the quality side: even with full context available, models fail to weight it correctly. Together they suggest that raw context length is necessary but insufficient. The DISCO paper from the same day also grapples with signal preservation under noise (over-smoothing in graphs), indicating a broader pattern across representation learning where proximity bias drowns out distant structure.
If LYRA's heavy-tailed attention mechanism maintains its benchmark gains when evaluated on LongBench-v2 variants that explicitly randomize irrelevant passage placement (rather than using natural document order), that confirms the noise hypothesis. If performance degrades significantly under randomization, the mechanism may be exploiting document structure rather than solving the underlying attention problem.
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MentionsLYRA · LongBench-v2 · RULER · LongBench
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Modelwire summarizes, we don’t republish. arXiv cs.LG originally reported this story as “The Sirens' Song: When Proximal Background Context Overshadows Distant Evidence”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.