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Showing papers from Tel Aviv University Show all papers

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Mean-Field Parallel Decoding for Discrete Diffusion Language Models

Tamim Zoabi, Ameen A Ali, Liran Ringel, Lior Wolf

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

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lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
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A Memory Efficient Unified Algorithm for Online Learning of Linear Dynamical Systems

Yuval Ran-Milo, Angelos Assos, Elad Hazan

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

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CryptanalysisBench: Can LLMs do cryptanalysis?

Lukas Fluri, Avital Shafran, Nicholas Carlini, Matthew Jagielski and 4 more

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

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One Temperature to Rule Them All?

Aviv Orly, Ori Shem Ur, Yaron Oz

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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57%Worth a look
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Locality Sensitive Hashing for p-Exponential Kernels with Applications to Density Estimation

Barak Gorodissky, Tal Wagner

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

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lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
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When Empathy Misses the Goal: A Benchmark for Goal Displacement in LLM Advice

Dean Ariel, Guy Laban

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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The Panel Complexity of Sortition: Is 12 Angry Men Enough?

Johannes Brustle, Simone Fioravanti, Tomasz Ponitka, Jeremy Vollen

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

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57%Worth a look
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Semantic Concept Steering Breaks the Explanation Drift Loop in Continual Learning

Yehonatan Elisha, Oren Barkan, Noam Koenigstein

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

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lenient 1/5
medium 0/10
strict 0/5
76%Highly rated
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Near-Optimal Stochastic Linear Bandits with Delay

Stochastic linear bandits with delayed feedback yield near-optimal, dimension-free additive penalties for loss-independent delays but dimension-dependent penalties for loss-dependent delays, unlike multi-armed bandits.

Ofir Schlisselberg, Mengxiao Zhang, Yishay Mansour

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

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AI panel: 10 of 20 reviewers recommend it
lenient 2/5
medium 4/10
strict 4/5
76%Highly rated
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Positional LSH: Binary Block Matrix Approximation for Attention with Linear Biases

Positional LSH represents ALiBi's bias matrix via binary block masks, yielding near-linear approximate attention with uniform accuracy across inputs.

Daniel Wolfson, Tal Wagner

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

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AI panel: 10 of 20 reviewers recommend it
lenient 3/5
medium 5/10
strict 2/5
80%Must read
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TROPT: An Open Framework for Unifying and Advancing Discrete Text Optimization

TROPT unifies discrete text optimization via a modular open-source framework with 30+ recipes, enabling cross-domain optimizer comparison, enhancement, and portability.

Matan Ben-Tov, Mahmood Sharif

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · Code ★ 11

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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 6/10
strict 1/5
83%Must read
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Routers Learn the Geometry of Their Experts: Geometric Coupling in Sparse Mixture-of-Experts

Geometric coupling aligns router and expert gradients along shared input directions in sparse Mixture-of-Experts, while load-balancing losses disrupt it, and cosine-similarity routing achieves low imbalance with minimal perplexity cost.

Sagi Ahrac, Noya Hochwald, Mor Geva

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

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13/20 AI panelreviewers recommend it

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AI panel: 13 of 20 reviewers recommend it
lenient 4/5
medium 8/10
strict 1/5
71%Highly rated
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Playing Markov Games Without Observing Payoffs

The paper introduces symmetric zero-sum Markov games and shows that observing only opponent actions allows asymptotically matching adversarial returns without payoff observations via online learning.

Daniel Ablin, Alon Peled-Cohen

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

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7/20 AI panelreviewers recommend it

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AI panel: 7 of 20 reviewers recommend it
lenient 3/5
medium 2/10
strict 2/5
76%Highly rated
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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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AI panel: 10 of 20 reviewers recommend it
lenient 4/5
medium 5/10
strict 1/5
70%Highly rated
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Epistemic Pairwise Maximin Share

Epistemic pairwise maximin share (EPMMS) relaxes PMMS fairness; 4/5-EPMMS allocations exist for additive valuations, exact EPMMS for bivalued valuations, and existence holds for three additive or two-type agents despite MMS nonexistence.

Michal Feldman, Amos Fiat, Yael Nissan, Tomasz Ponitka

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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5/20 AI panelreviewers recommend it

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AI panel: 5 of 20 reviewers recommend it
lenient 0/5
medium 3/10
strict 2/5
76%Highly rated
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On the Recall Scaling Laws in Mamba: A Theoretical and Mechanistic Study via Hashing

Mamba performs associative recall via implicit linear hashing, and Recall Scaling Laws predict required dimensions and success probabilities for perfect recall.

Yuval Koren, Assaf Ben-Kish, Raja Giryes, Lior Wolf and 1 more

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

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AI panel: 10 of 20 reviewers recommend it
lenient 4/5
medium 6/10
strict 0/5
86%Must read
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Outcome-Based RL Provably Leads Transformers to Reason, but Only With the Right Data

Outcome-based RL provably teaches single-layer transformers iterative graph traversal via chain-of-thought, but only with sufficient simple training examples.

Yuval Ran-Milo, ‪Yotam Alexander‬‏, Shahar Mendel, Nadav Cohen

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

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14/20 AI panelreviewers recommend it

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AI panel: 14 of 20 reviewers recommend it
lenient 5/5
medium 6/10
strict 3/5
78%Highly rated
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ParetoSlider: Diffusion Models Post-Training for Continuous Reward Control

ParetoSlider trains one diffusion model with continuous preference weights to approximate the full Pareto front, enabling inference-time navigation of trade-offs between conflicting generative goals without retraining.

Shelly Golan, Michael Finkelson, Ariel Bereslavsky, Yotam Nitzan and 1 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 15 on Hugging Face

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11/20 AI panelreviewers recommend it

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AI panel: 11 of 20 reviewers recommend it
lenient 5/5
medium 6/10
strict 0/5
88%Must read
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A simple model of co-emergence of grid and place fields

A single sensory-prediction recurrent network with Dale's Law co-emerges grid and place cells without supervision, reproducing key spatial coding phenomena.

Zhaoze Wang, Genela Morris, Dori Derdikman, Pratik Chaudhari and 1 more

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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15/20 AI panelreviewers recommend it

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AI panel: 15 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 3/5
88%Must read
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TrajLoc: Trajectory-Attention Localization for Multi-Object Motion Control

TrajLoc isolates per-object attention via Gaussian heatmaps to control multi-object motion, improving trajectory adherence by 51% and PSNR by 4.3 dB.

Omer Sela, Inbar Huberman-Spiegelglas, Michael Rotman, Sagie Benaim and 1 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 1 on Hugging Face · Code ★ 2

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AI panel: 15 of 20 reviewers recommend it
lenient 4/5
medium 8/10
strict 3/5