Good Papers

Showing papers from Boston University Show all papers

45%Niche pick
?Niche pickVote to see the score

Learning Cultural Vectors for Cross-Cultural Generation

Sina Malakouti, Deepti Ghadiyaram, Boqing Gong, Adriana Kovashka

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

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score

Learning to Undo: Transfer Reinforcement Learning under State Space Transformations

Mridul Mahajan, Aldo Pacchiano, Xuezhou Zhang

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score

Autonomous Driving Research Requires a Community-Driven Data Paradigm

Jinsu Yoo, Zanming Huang, Katie Luo, Zheda Mai and 4 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score

A Transformer-Derived Iterative Preconditioner

Patrick Lutz, Themistoklis Haris, Aditya Gangrade, Venkatesh Saligrama

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

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
74%Highly rated
?Highly ratedVote to see the score

Two Stages of Folding: Convergent Mechanisms in AI Protein Folding Trunks

Protein folding models share a two-stage trunk mechanism initializing biochemical signals then spatial features, with causally steerable, interchangeable representations across architectures.

Kevin Lu, Jannik Brinkmann, Stefan T Huber, Aaron Mueller and 3 more

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

– ReadersNo votes yet
9/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 9 of 20 reviewers recommend it
lenient 3/5
medium 5/10
strict 1/5
83%Must read
?Must readVote to see the score

Swift Sampling: Selecting Temporal Surprises via Taylor Series

Swift Sampling uses Taylor-series projections of visual feature trajectories to select temporally surprising frames, cutting overhead by 30x while boosting long-video accuracy up to 12.5 points.

Dahye Kim, Bhuvan Sachdeva, Karan Uppal, Naman Gupta and 2 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026 · ▲ 7 on Hugging Face

– ReadersNo votes yet
13/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 13 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 1/5
78%Highly rated
?Highly ratedVote to see the score

A Theory of Training Profit-Optimal LLMs

Economic model combining scaling laws with microeconomics shows profit-optimal LLM training scales near-linearly with hardware efficiency and sub-quadratically in cost, while current expenditure trends are only optimal under compute-bound assumptions.

Sophie Hao, Will Merrill

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

– ReadersNo votes yet
11/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 11 of 20 reviewers recommend it
lenient 5/5
medium 5/10
strict 1/5
78%Highly rated
?Highly ratedVote to see the score

Convergence Analysis of Newton's Method for Neural Networks in the Overparameterized Limit

Regularized Newton training of overparameterized neural networks converges to a deterministic NNTK limit with exponentially fast uniform convergence across all frequencies, avoiding gradient descent's spectral bias.

Konstantin Riedl, Justin Sirignano, Konstantinos Spiliopoulos

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

– ReadersNo votes yet
11/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 11 of 20 reviewers recommend it
lenient 3/5
medium 6/10
strict 2/5
74%Highly rated
?Highly ratedVote to see the score

A Unified Framework for Adversary-Aware Differential Privacy Bounds

A unified framework bounds DP privacy leakage against multi-target membership, attribute, and reconstruction attacks using only privacy parameters and adversarial baseline success rates.

Marika Swanberg, Meenatchi Sundaram Muthu Selva Annamalai, Jamie Hayes, Borja Balle and 1 more

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

– ReadersNo votes yet
9/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 9 of 20 reviewers recommend it
lenient 4/5
medium 5/10
strict 0/5
83%Must read
?Must readVote to see the score

GenScale: A Benchmark for Relative Object Scale in Image Generation and Editing

GenScale benchmarks relative object scale in image generation and editing, finding current models unreliable, while Rescale improves scale plausibility via localized correction.

Lingxiao Li, Max Whitton, Ledell Wu, Boqing Gong

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

– ReadersNo votes yet
13/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 13 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 1/5
80%Must read
?Must readVote to see the score

The Curse of Multiple Mediators: Hidden Interaction Effects in Activation Patching

Activation patching's natural indirect effect embeds hidden interaction effects between components, which cause conditional importance to be invisible or inflated, explain faithfulness instability, scale with activation distance, and diagnose when greedy component ranking misses combinatorial mechan

Sankaran Vaidyanathan, David Arbour, Aaron Mueller, Scott Niekum and 1 more

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

– ReadersNo votes yet
12/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 12 of 20 reviewers recommend it
lenient 2/5
medium 7/10
strict 3/5
74%Highly rated
?Highly ratedVote to see the score

Consistency-Preserving Concept Erasure via Unsafe–Safe Pairing and Directional Fisher-weighted Adaptation

PAIR reframes diffusion model concept erasure via unsafe-safe pairs, using paired semantic realignment and directional Fisher-weighted adaptation to remove targeted concepts while preserving structural and semantic consistency.

Yongwoo Kim, Sungmin Cha, Hyunsoo Kim, Jaewon Lee and 1 more

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

– ReadersNo votes yet
9/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 9 of 20 reviewers recommend it
lenient 5/5
medium 4/10
strict 0/5
76%Highly rated
?Highly ratedVote to see the score

ReToken: One Token to Improve Vision–Language Models for Visual Retrieval

ReToken introduces one learnable retrieval token that selects sparse visual tokens from long contexts, improving vision-language models by up to 13.4 points on visual retrieval while fitting on a single GPU.

Yao Xiao, Reuben Tan, Zhen Zhu, Yuqun Wu and 2 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026 · ▲ 8 on Hugging Face · Code ★ 18

– ReadersNo votes yet
10/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 10 of 20 reviewers recommend it
lenient 4/5
medium 5/10
strict 1/5
80%Must read
?Must readVote to see the score

Finding Interpretable Prompt-Specific Circuits in Language Models

ACC++ improves circuit tracing to extract interpretable prompt-specific language model circuits from single passes, revealing clustered indirect-object mechanisms and language-specific reused components.

Gabriel Franco, Lucas M Tassis, Azalea Rohr, Mark Crovella

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

– ReadersNo votes yet
12/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 12 of 20 reviewers recommend it
lenient 3/5
medium 8/10
strict 1/5