Good Papers

Showing papers from School of Computer and Communication Sciences, EPFL - EPF Lausanne Show all papers

45%Niche pick
?Niche pickVote to see the score

AI Control for Sandbagging on Fuzzy Tasks

Mikhail Terekhov, Caglar Gulcehre, Vivek Hebbar, Joe Benton

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
80%Must read
?Must readVote to see the score

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization

Joint Consistency frames test-time aggregation as energy minimization using pairwise interactions and evaluation signals, outperforming existing voting methods across reasoning benchmarks.

Yunzhen Yao, Hongye Wang, Yahong Wang, Michael Gastpar and 2 more

Atlanta Poster Session 5, Fri, Dec 11, 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 4/5
medium 7/10
strict 1/5
88%Must read
?Must readVote to see the score

Your Neighbors Know: Leveraging Local Neighborhoods for Backdoor Detection in Decentralized Learning

Argus detects backdoor attacks in decentralized learning by having nodes share local trigger analyses with neighbors and filter updates via structural similarity, reducing attack success by up to 90 points without a central server.

Sayan Biswas, Antoine Boutet, Davide Frey, Romaric Gaudel and 6 more

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · 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 9/10
strict 2/5
86%Must read
?Must readVote to see the score

TERMINATOR: Learning Optimal Exit Points for Early Stopping in Chain-of-Thought Reasoning

Terminator learns optimal early-exit points for chain-of-thought reasoning to cut token lengths by 14%-55% and boost inference speed over 2x with minimal accuracy loss.

Alliot Nagle, Jakhongir Saydaliev, Dhia Garbaya, Michael Gastpar and 2 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 19 on Hugging Face

– ReadersNo votes yet
14/20 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: 14 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 1/5