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@stellalisy
PhD student @uwnlp | visiting researcher @AIatMeta | prev @jhuclsp
AI
A user describes Boba as curious, playful and imaginative, and Instinct as precise, self-auditing and dryly funny.
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humans& says its Ampersand model powers Boba, which it pitches for reminders, group planning and web research.
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Its creators say video reward models struggle with shuffled or held frames, even though humans can perceive the changes.
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Its creators say ExploreNet adjusts its exploration distribution to the current state, with rewards tied to rollout diversity.
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A post introducing HorizonBench describes a benchmark and data generator for tracking user preferences as life events change them.
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A user lists personalization, preference modeling and long-horizon human-agent and multi-agent interactions as research interests.
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Two posts list HorizonBench for a lifelong-agent workshop and a multilingual reasoning session for a generative AI workshop.
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A prospective attendee lists a chatbot-ads poster at 4:40 p.m. PDT and an October 9 spotlight talk on LLM moral safety.
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A post describing a Bloomberg Live clip says InterPositive shot its own dataset on a controlled stage for late-stage training.
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A post introducing SWE-sweep says top models score below 5% on the benchmark and calls it one of its creators’ most challenging.
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Its creator says the benchmark pairs 10,000 synthetic scene instructions with program verifiers. They report that frontier API models scored at most 22% on a harder challenge set.
