We have 4 of 11 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
Looped Transformers improve parameter efficiency by repeatedly applying shared Transformer blocks over multiple recurrent loops, increasing computational depth without increasing the parameter count. However, KV cache memory still scales with the number of loops, becoming a key memory bottleneck that limits batch size...
Reasoning capabilities are critical for advancing Large Language Models, yet current approaches either require massive computational budgets or struggle to effectively distill reasoning to smaller models. Standard distillation methods rely on outcome-based rewards, failing to distinguish between sound reasoning and luc...
This work proposes LoopSpec, a training-free self-speculative decoding framework tailored for Looped Transformers that introduces a selective second proposal from deeper recurrent depth while ensuring lossless decoding under both greedy and sampling regimes.
A composable, domain-specific backend, Pramana, shaped as a thin waist, with diverse research intents at the top and disparate execution substrates at the bottom is envisioned, and Pramana's utility is demonstrated by building a first-of-its-kind corpus of 255 data-generation intents mined from 66 published papers.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
