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Context Language Models¶
L1 core mechanism diagnostic. Context-file editing and suffix cache boundaries run; Qwen3.5-9B was not trained and no 24-hour agent swarm ran.
Paper information¶
| Field | Details |
|---|---|
| Paper | arXiv 2609.37725 |
| Institutions | University of Washington / Meta Superintelligence Labs (first author affiliations) |
| First public release | 2026-09-29 (arXiv v1) |
| Original source code | Yes: facebookresearch/context-language-models |
| Adapter | context-lm |
| Local reproduction code | src/auto_research/agent_research/latest_20261001.py |
Original paper summary¶
Background and main changes¶
Treat context as a file the model can edit directly, letting it retain, delete or reorganize information instead of following a fixed external harness policy. Multiple agents maintain separate context files, and the server prefills from the first mismatching token.
flowchart LR
I[Public input and current state] --> M[Context-file mechanism]
M --> A[Auditable intermediate values]
A --> O[Output or state update]
Key figure from the paper¶
Figure 28: Training flow and key optimizations. From the paper; copyright belongs to the authors. Click for the source.
Core equations¶
\(c_{t+1}=f_\theta^{CLM}(c_t)\). Locally, replace/append file transforms enforce explicit capacity and reuse cache only up to the first prefix mismatch, recomputing the suffix.
Offline and online results¶
BrowseComp-Plus accuracy improved 11.4% with 21.5% fewer FLOPs; EdgeBench score improved 5% with 59% fewer FLOPs. Online RL improved Qwen3.5-9B by 47.6% with 12% fewer FLOPs.
Local reproduction¶
Comparison protocol: Disabled or default mechanism versus enabled core operator. Only invariants and state transitions are checked; cross-model relative change is not applicable.
See metrics/mechanism-seeds42-44.json for the three-seed diagnostic. diagnostic_only=true means executable state transitions, gradients or scheduling invariants, not formal capability ranking.
Scope¶
Context-file edits and suffix cache boundaries execute. No Qwen3.5-9B training or 24-hour agent swarm.
