Generative Adversarial Networks
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A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
No Canadian affiliation. An affiliation-only frame — the usual design — would never have seen this work. It is one of the works that make the case for inverting the frame.
Abstract
Large Language Models (LLMS) rely on Key-Value (KV) caches to store attention context during autoregressive decoding. In long-sequence settings, the KV cache can consume large amounts of VRAM and become a practical bottleneck for throughput . We introduce KVHALO, an auxiliary reconstruction model that restores higher-fidelity KV tensors from a compressed cache state when required, reducing persistent memory footprint during inference. In our evaluation, KVHALO achieves up to 91.85% directional cosine alignment at convergence and reduces long-context degradation relative to a low-bit baseline under our stress-test workloads. We used HRM instead of other architectures, which allowed for higher-quality results in only 18,600 steps.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- arXiv (Cornell University)
- Topic
- Generative Adversarial Networks and Image Synthesis
- Field
- Computer Science
- Canadian institutions
- —
- Funders
- Compute CanadaCanada Research ChairsCanadian Institute for Advanced Research
- Keywords
- Discriminative modelMinimaxComputer scienceInferenceArtificial intelligencePerceptronGenerative grammarMachine learningSample (material)Generative modelMistakeArtificial neural networkMathematical optimizationMathematics
- Has abstract in OpenAlex
- yes