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Showing papers from School of Engineering and Applied Sciences, Harvard University Show all papers

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The parameters in weight-sparse transformers are interpretable

Arnau Marin-Llobet, Stefan Heimersheim

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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lenient 0/5
medium 0/10
strict 0/5
80%Must read
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AI GAMESTORE: Scalable, Open-Ended Evaluation of Machine General Intelligence with Human Games

AI GameStore proposes evaluating general intelligence via scalable synthesis of human games, finding frontier vision-language models score under 10% of human averages on most generated games.

Lance Ying, Ryan Truong, Prafull Sharma, Kaiya Zhao and 8 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026 · ▲ 9 on Hugging Face

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AI panel: 12 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 0/5
88%Must read
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Temporal Backtracking Search for Test-time Generative Video Reasoning

Temporal Backtracking Search improves video reasoning by searching over the temporal axis and restarting from verified prefixes rather than resampling from scratch, achieving 22.7% versus 0.7% best-of-N out-of-distribution.

SeJoon Jun, Zheng Ding, Huangyuan Su, Weirui Ye and 1 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 15 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 2/5
86%Must read
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Economy of Minds: Emerging Multi-Agent Intelligence with Economic Interactions

A decentralized agent economy using auctions and economic selection emerges multi-step reasoning and outperforms monolithic baselines without centralized coordination.

Zhenting Qi, Ao Qu, Huangyuan Su, Chenyu Wang and 12 more

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026 · ▲ 10 on Hugging Face · Code ★ 59

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AI panel: 14 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 2/5
89%Must read
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Geometry-Adaptive Explainer for Faithful Dictionary-Based Interpretability under Distribution Shift

Out-of-distribution shifts rotate active subspaces, misaligning dictionary explainers; a geometry-adaptive realignment using unlabeled OOD activations closes the faithfulness gap and restores causal interpretability without training.

Sungjun Lim, Heedong Kim, Andrew Lee, Kyungwoo Song

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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16/20 AI panelreviewers recommend it

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AI panel: 16 of 20 reviewers recommend it
lenient 4/5
medium 9/10
strict 3/5
80%Must read
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LiFT: Lifted Inter-slice Feature Trajectories for 3D Image Generation from 2D Generators

LiFT generates high-resolution 3D medical images via inter-slice feature trajectories, achieving strong coherence with much lower inference cost.

Xinhe Zhang, Yuyang Zhang, Pengfei Jin, Arnau Marin-Llobet and 2 more

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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AI panel: 12 of 20 reviewers recommend it
lenient 5/5
medium 6/10
strict 1/5
71%Highly rated
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Incorporating Hierarchical Semantics in Sparse Autoencoder Architectures

A hierarchical sparse autoencoder architecture explicitly models semantic concept hierarchies, improving reconstruction, interpretability, and efficiency in language model representations.

Mark Muchane, Sean M Richardson, Kiho Park, Victor Veitch

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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6/20 AI panelreviewers recommend it

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AI panel: 6 of 20 reviewers recommend it
lenient 4/5
medium 2/10
strict 0/5