YANchor-4B reaches AIME performance with linear time and constant memory
Source published ·Modelwire updated
Original coverage: arXiv cs.CL ↗·How Modelwire adds context
The development
YANchor-4B addresses a fundamental constraint in long-context reasoning: the memory and compute wall that forces models to choose between fidelity and efficiency. By anchoring key information in a compressed recurrent state, the model achieves linear-time generation with constant memory while maintaining reasoning quality on hard problems like AIME and HMMT. This matters because it suggests a viable path beyond attention's quadratic scaling without sacrificing the precision that makes math and code reasoning reliable. For practitioners building long-horizon systems, this represents a meaningful efficiency frontier shift.
Modelwire’s AI-generated summary of coverage from arXiv cs.CL.
Modelwire analysis
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YANchor-4B's key contribution isn't just efficiency but the mechanism: it maintains reasoning fidelity by selectively anchoring compressed state rather than uniformly quantizing or discarding context. This is distinct from aggressive KV cache compression, which trades precision for density.
The prior two weeks have surfaced multiple angles on the long-context inference bottleneck. TaSQ (Oct 2) attacked KV cache compression through quantization space reshaping, while ReFold (Oct 6) proposed lossless context folding to extend agent horizons. YANchor-4B takes a different path: recurrent state compression with selective anchoring. Where TaSQ and ReFold optimize within existing attention architectures, this work proposes an alternative compute model entirely. The question is whether the reasoning quality holds across domains beyond math benchmarks, since AIME and HMMT are narrow evaluation targets compared to the clinical reasoning rubric gap identified in the Oct 1 review.
If YANchor-4B's reasoning quality (measured by F1 or accuracy on out-of-distribution reasoning tasks like GSM8K or DROP) remains within 2-3 percentage points of full-attention baselines at 32K+ context windows, the recurrent approach becomes a genuine alternative to attention. If the gap widens beyond 5 points, it signals the anchoring mechanism loses critical information under real-world reasoning loads.
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Coverage behind this analysis
These archive entries ground the connection in our analysis. They are ordered by source publication date, with links to our coverage and the original sources.
·arXiv cs.CL
New quantization method tackles 1-bit KV cache compression for long-context LLMs
Extreme compression of key-value caches in long-context LLM inference has hit a wall at 1-bit quantization, where existing methods fail to preserve model quality. Researchers introduce TaSQ, a technique that reshapes the quantization space through query-weighted channels, cross-head normalization, and covariance-aware grouping to recover accuracy under aggressive compression. This addresses a critical bottleneck in serving…
MentionsYANchor-4B · AIME · HMMT
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Modelwire summarizes, we don’t republish. arXiv cs.CL originally reported this story as “YANchor-4B: Effective Long-Horizon Reasoning in O(N) Time with O(1) Memory”. 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.