
Jerry Zhi-Yang He
@_herobotics_
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LLM research @ Bytedance Seed. prev. PhD at @berkeley_ai with @ancadianadragan, @facebookai, @StanfordSVL and @StanfordHRI.
Stanford, CA
Joined November 2014
Is your robot 🤖 safe around humans🚸? Or will it fail catastrophically🤕? . Excited to introduce "Natural Adversarial Frontier", a framework for probing the robustness of Human-Robot Interactions. To appear at #CoRL2023 🧵(1/8).Joint work with @ancadianadragan @daniel_s_brown
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RT @jaseweston: 🤖Introducing: CoT-Self-Instruct 🤖.📝: - Builds high-quality synthetic data via reasoning CoT + quali….
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RT @GuilleAngeris: looks like Lean is popular today, so here's a little post on how/why it works and implementing a mini version of it in J….
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RT @denny_zhou: Slides for my lecture “LLM Reasoning” at Stanford CS 25: Key points: .1. Reasoning in LLMs simply….
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RT @PrincetonCS: ⏱️AI is making verification process easier, with models verifying proofs in minutes. 💻 Now, @prfsanjeevarora, @chijinML,….
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RT @OwainEvans_UK: New paper & surprising result. LLMs transmit traits to other models via hidden signals in data. Datasets consisting only….
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RT @rosemary_ke: TLDR: Gemini answered 5 out of 6 questions correctly, also within the time frame of 4.5 hours. Read the natural language….
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RT @DimitrisPapail: Speculation: Within a year a <100B open weights model will also solve 5/6 IMO problems.
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RT @prfsanjeevarora: Completely misses the point. Nobody is suggesting that solving IMO problems is useful for math research. The point is….
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RT @paulcbogdan: New paper: What happens when an LLM reasons?. We created methods to interpret reasoning steps & their connections: resampl….
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RT @_kevinlu: The internet is incredibly diverse, and it is sourced from data on topics which. humans actually cared about to engage with….
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RT @IntuitMachine: Baidu's Ernie 4.5 and ByteDance's Seed 1.6 Thinking projects are really cooking!
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RT @Rainmaker1973: A typical Japanese scene. It's sad to see a machine that has worked for many years go away. The 92-year-old former fac….
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RT @AnthropicAI: New on the Anthropic Engineering blog: how we built Claude’s research capabilities using multiple agents working in parall….
anthropic.com
On the the engineering challenges and lessons learned from building Claude's Research system
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RT @bilawalsidhu: Lmao. What niche even is this — grassroots dirt track racing meets google maps nerds?. Veo 3 videos are seriously ridicul….
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RT @layer07_yuxi: @ElliotGlazer We remain unsure if AlphaEvolve can create new reusable concepts in combinatorial optimization, such as res….
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RT @mikeknoop: ARC-AGI o3 retest results are in!. Takeaway: o3 (medium) is the industry leading AI reasoning system by large margin. 2X sco….
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Just in case you want to explain Newton's Law to a baby, come check out how LLM does it at our #ICLR2025 poster tmr!.
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My takeaway from the project is that LLMs have a highly composable and steerable latent space. Very excited for progress and applications in controllable generations.
Check out our #ICLR2025 paper on Context Steering, where we present an inference-time method that modulates the level of contextual inference for controllable generation ✍️. Joint work with @_herobotics_ @mariah17schrum @ancadianadragan
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