Good Papers

Showing papers from Georgia Tech Show all papers

45%Niche pick
?Niche pickVote to see the score

Hypothesis generation and updating in large language models

Huadong Xiong

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 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

GRASP: Learning to Ground Social Reasoning in Multi-Person Non-Verbal Interactions

Junho Kim, Xu Cao, Houze Yang, Bikram Boote and 5 more

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

Coupling-Aware Reinforcement Learning for Co-Evolving Graph Games

Mina Kim, Guanghui Lan, Benoit Montreuil

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

Robust Satisficing Ensemble: Scalable Model Aggregation Under Distribution Shifts

Ahmet Faruk Cetinkaya, Enes Ağırman, Cem Tekin

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

TimeOperator: A Function-to-Function Approach to Time Series Modeling

SheoYon Jhin, B. Aditya Prakash, Noseong Park

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · 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
67%Highly rated
?Highly ratedVote to see the score

FETTUCCINE: Fast and efficient brain-to-text decoding on mobile devices

Jonathan McCart, Pranav Deevi, Mehdi Azabou, Nanda H Krishna and 4 more

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

– ReadersNo votes yet
2/20 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: 2 of 20 reviewers recommend it
lenient 2/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score

On the Efficiency of Structured Pruning in Small Language Model Pretraining

Yixiao Li, Xianzhi Du, AJAY JAISWAL, Tao Lei and 3 more

Atlanta Poster Session 3, Thu, Dec 10, 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
71%Highly rated
?Highly ratedVote to see the score

Learning Theory of Transformers: Local-to-Global Approximation via Softmax Partition of Unity

Transformers approximate α-Hölder functions via softmax partition of unity with two encoder blocks, achieving near minimax-optimal generalization rates.

Zhongjie Shi, Wenjing Liao

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

– ReadersNo votes yet
7/20 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: 7 of 20 reviewers recommend it
lenient 2/5
medium 4/10
strict 1/5
89%Must read
?Must readVote to see the score

Less Decoder is More Encoder: Geometric Representation Learning from Novel View Synthesis

SNAP uses a pose-conditioned local decoder and latent-space reconstruction objective for self-supervised geometric representation learning via novel view synthesis, yielding transferable multi-view features competitive with supervised methods.

Keerthi Kaashyap, Dennis Anthony, Akshay Krishnan, Nhi Nguyen and 4 more

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

– ReadersNo votes yet
16/20 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: 16 of 20 reviewers recommend it
lenient 5/5
medium 10/10
strict 1/5
88%Must read
?Must readVote to see the score

Soft Token Alignment for Cross-Lingual Reasoning

SOLAR aligns soft-token representations across languages during supervised fine-tuning to improve multilingual reasoning consistency, boosting accuracy up to 17.7 points with largest gains on low-resource languages.

Ivy He, Jungsoo Park, Wei "Coco" Xu, Alan Ritter

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

– ReadersNo votes yet
15/20 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: 15 of 20 reviewers recommend it
lenient 4/5
medium 10/10
strict 1/5
88%Must read
?Must readVote to see the score

Bounding Global and Local Compression Error of Signal Parameterizations

A framework predicts reconstruction error of compressive signal parameterizations via scaled differences between model predictions at different compression levels without ground truth. It yields non-asymptotic, signal-specific bounds that closely track global errors and local error heatmaps across i

Quang Luong Nhat Nguyen, Sara Fridovich-Keil

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

– ReadersNo votes yet
15/20 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: 15 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 2/5
72%Highly rated
?Highly ratedVote to see the score

Actor-Accelerated Policy Dual Averaging for Reinforcement Learning in Continuous Action Spaces

Actor-accelerated PDA learns a policy network to approximate PDA optimization subproblems, speeding up continuous-action reinforcement learning while preserving convergence guarantees and outperforming PPO.

Ji Gao, Caleb Ju, Guanghui Lan, Zhaohui Tong

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

– ReadersNo votes yet
8/20 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: 8 of 20 reviewers recommend it
lenient 4/5
medium 4/10
strict 0/5