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TUE 6 OCT
8:45 a.m.
9 a.m.
10 a.m.
Orals 10:00-11:00
[10:00]
Source-Modality Monitoring in Vision-Language Models
[10:15]
CollabSkill: Evaluating Human-Agent Collaboration On Real-World Tasks
[10:30]
Reasoning about Intent for Ambiguous Requests
[10:45]
More Than Words: Compositional Tokenization for Efficient Language Models
(ends 11:00 AM)
11 a.m.
2:30 p.m.
3:30 p.m.
Orals 3:30-4:30
[3:30]
A Unified View of Attention and Residual Sinks: Outlier-Driven Rescaling is Essential for Large Language Model Training
[3:45]
When Do LLMs Admit Their Mistakes? Understanding The Role Of Model Belief In Retraction
[4:00]
Phonological Perception of Sign Language Models
[4:15]
Attribution Bias in Large Language Models
(ends 4:30 PM)
4:30 p.m.
Posters 4:30-6:30
(ends 6:30 PM)
WED 7 OCT
9 a.m.
10 a.m.
Orals 10:00-11:00
[10:00]
Rethinking Generalization in Reasoning SFT: A Conditional Analysis on Optimization, Data, and Model Capability
[10:15]
What Do Language Models Learn and When? The Implicit Curriculum Hypothesis
[10:30]
BugScope: Learn to Detect Bugs Like Human
[10:45]
Do Humans and LLMs Diverge in Belief Revision? Evidence from a Bayesian Analysis
(ends 11:00 AM)
11 a.m.
(ends 1:00 PM)
1 p.m.
2:30 p.m.
3:30 p.m.
Orals 3:30-4:30
[3:30]
How Can We Synthesize High-Quality Pretraining Data? A Systematic Study of Prompt Design, Generator Model, and Source Data
[3:45]
Beyond Distribution Sharpening: The Importance of Task Rewards
[4:00]
Beyond the Rosetta Stone: Unification Forces in Generalization Dynamics
[4:15]
Distributed Attacks in Persistent-State AI Control
(ends 4:30 PM)
4:30 p.m.
Posters 4:30-6:30
(ends 6:30 PM)
THU 8 OCT
9 a.m.
10 a.m.
Orals 10:00-11:00
[10:00]
Extracting memorized pieces of (copyrighted) books from open-weight language models
[10:15]
Alignment Whack-a-Mole : Finetuning Activates Verbatim Recall of Copyrighted Books in Large Language Models
[10:30]
Message Passing Enables Efficient Reasoning
[10:45]
A Mirage of Coherence: How Metaphor Impacts Language Models' Discourse Coherence Assessment
(ends 11:00 AM)
11 a.m.
(ends 1:00 PM)
2:30 p.m.
3:30 p.m.
Orals 3:30-4:30
[3:30]
Diversity or Precision? A Deep Dive into Next Token Prediction
[3:45]
MEMENTO: Teaching LLMs to Manage Their Context
[4:00]
What AI Benchmarks Actually Measure: Adapting Convergent and Discriminant Validity to Interrogate Fifty-Six AI Benchmarks
[4:15]
Introspective Diffusion Language Models
(ends 4:30 PM)
4:30 p.m.
4:40 p.m.
(ends 6:30 PM)
FRI 9 OCT
8:30 a.m.
Workshop:
(ends 6:00 PM)
Workshop:
(ends 6:00 PM)
Workshop:
(ends 6:00 PM)
Workshop:
(ends 6:00 PM)
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