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57%Worth a look
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Accelerating the Inference Era with AI-Driven, Globally Optimized HW/SW Co-Design

Miria Feng, Fangzhao Zhang, Adrian G Lafuente, Mert Pilanci and 1 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
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Speculative Self-Distillation enables Efficient Knowledge Internalization

Shayan Talaei, Agam Bhatia, Arshia Soltani Moakhar, Jonas Hübotter and 2 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
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Hawkeye: Hardware-Aware GPU Kernel Optimization with Minimal Supervision

Arya Tschand, Kesavan Ramakrishnan, Alexander Ingare, Simon Guo and 5 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 0 of 20 reviewers recommend it
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medium 0/10
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89%Must read
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Intelligence per Watt: Measuring Intelligence Efficiency of Local AI

Proposing intelligence per watt to evaluate local LLM inference, the study finds local models answer 88.7% of queries with 5.3x efficiency gains since 2023 but remain 1.4x less efficient than cloud accelerators.

Jon Saad-Falcon, Avanika Narayan, Hakki Akengin, J. W Griffin and 10 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 17 on Hugging Face · Code ★ 95

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16/20 AI panelreviewers recommend it

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AI panel: 16 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 4/5
91%Must read
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TokenSwap: Benchmarking and Reducing the Modality Gap in Multimodal LLMs

TokenSwap benchmarks and reduces MLLMs' modality gap by interleaving visual tokens with text, finding reasoning models have smaller gaps and training with TokenSwap mitigates it.

Andong Hua, Colton Bishop, Igor Mordatch, Arian Hosseini and 4 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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18/20 AI panelreviewers recommend it

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AI panel: 18 of 20 reviewers recommend it
lenient 4/5
medium 9/10
strict 5/5
74%Highly rated
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Dual-Rate Diffusion: Accelerating diffusion models with an interleaved heavy-light network

Dual-Rate Diffusion accelerates diffusion inference by interleaving sparse heavy context encoders with light denoising models, cutting computation 2-4x without quality loss.

Grigory Bartosh, David Ruhe, Emiel Hoogeboom, Jonathan Heek and 2 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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9/20 AI panelreviewers recommend it

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AI panel: 9 of 20 reviewers recommend it
lenient 4/5
medium 5/10
strict 0/5