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Showing papers from Technion---Israel Institute of Technology Show all papers

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NeurIPS 2026TechnionU OxfordTechnion---Israel Institute of TAI oversight & deception

Selective Safety Steering via Value-Filtered Decoding

Value-filtered decoding selectively steers LLM generation using a value-based safety criterion with explicit false-intervention bounds, improving safety-utility trade-offs over baselines.

Bat-Sheva Einbinder, Hen Davidov, Yee Whye Teh, Yarin Gal and 1 more

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

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11/20 AI panelreviewers recommend it

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lenient 5/5
medium 5/10
strict 1/5
76%Highly rated
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NeurIPS 2026TechnionTel AvivTechnion---Israel Institute of TTest-time compute

Dependency-Guided Parallel Decoding in Discrete Diffusion Language Models

DEMASK predicts token dependencies in discrete diffusion language models to select weakly dependent masked positions for parallel unmasking, bounding sampling error and accelerating Dream-7B by 1.7, 2.2× with preserved accuracy.

Liran Ringel, Ameen A Ali, Yaniv Romano

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

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lenient 4/5
medium 5/10
strict 1/5
80%Must read
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NeurIPS 2026Technion - Israel Institute of TTechnion -- Israel Institute of Technion---Israel Institute of TControllable generation

When Metropolis and Hastings Meet Bradley and Terry: Exact MCMC From Preference Voting

Pref-MH samples from judge-conditioned distributions via exact Metropolis-Hastings using only stochastic binary pairwise preferences and converges to the target with optimal acceptance rules.

Ariel Smogorghevski, Nir Rosenfeld, Yaniv Romano

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

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12/20 AI panelreviewers recommend it

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AI panel: 12 of 20 reviewers recommend it
lenient 5/5
medium 5/10
strict 2/5
74%Highly rated
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NeurIPS 2026TechnionTechnion - Israel Institute of TTechnion---Israel Institute of TLLM evaluation & benchmarks

Valid Best-Model Identification for LLM Evaluation via Low-Rank Factorization

A framework combines multi-armed bandits with low-rank predictions to build doubly robust estimators and valid confidence intervals for identifying the best LLM with fewer evaluations.

Elad Tolochinsky, Yaniv Tenzer, Yaniv Romano

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 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 5/5
medium 4/10
strict 0/5