Jordan Hochenbaum @Jnatanh
Machine Learning | Data | Engineering | Sound | Art. Currently VP, AI @BetterUp. Co-founder @KadenzeOfficial, Previously @Twitter, Professor @CalArts. jordanhochenbaum.com New York, USA Joined June 2013-
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One of the best @latentspacepod pods ever with @RLanceMartin. Fantastic overview of everything context engineering for agents condensed into 1h. Kudos! open.spotify.com/episode/1QU7f8…
What if we could evolve AI models like organisms in nature, letting them compete, mate, and combine their strengths to produce ever-fitter offspring? Excited to share our new work: “Competition and Attraction Improve Model Fusion” presented at GECCO’25🦎 where it was a runner-up…
Enjoyed reading this new steering work on monitoring controlling for character traits in LLMs. Interestingly, when comparing pre and post-hoc steering methods, they found that steering the model toward various undesirable persona directions during finetuning was more effective at…
Reinforcement Learning Teachers of Test Time Scaling In this new paper, we introduce a new way to teach LLMs how to reason by learning to teach, not solve! The core idea: A teacher model is trained via RL to generate explanations from question-answer pairs, optimized to improve…
Many people assume that LRM reasoning breaks down past a certain "complexity" or "number of steps" threshold. This is incorrect. It breaks down past an unfamiliarity threshold. And that threshold is very low. There is no limit to the complexity of tasks you can solve with these…
When asked for feedback on work, I often frame it in terms of distance, speed, and velocity: • Distance: Total amount of work done • Speed: Time it took to do the work • Velocity: Moving in the right direction Teams that produce more output appear to contribute more. This is…
Could a major opportunity to improve representation in deep learning be hiding in plain sight? Check out our new position paper: Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis. The idea stems from a little-known…
+1 I know a lot of folks at various places that need to Terraform all the things to be compliant with there Infra team's requirements
+1 I know a lot of folks at various places that need to Terraform all the things to be compliant with there Infra team's requirements
Reciprocal Rank Fusion is not all you need for hybrid search What we need is query understand to use the right ranking solution for the job. softwaredoug.com/blog/2024/11/0…
🚀 Exciting news: ColQwen2, our latest SOTA visual retriever, now supports similarity maps with colpali-engine 0.3.2! Read this thread for more insights. (1/N 🧵) Can’t wait to try this out? Check out the companion cookbook here: github.com/tonywu71/colpa… 👀
🚨 New model alert: ColQwen2 ! It's ColPali, but with a Qwen2-VL backbone, making it the best visual retriever to date, topping the Vidore Leaderboard with a significant +5.1 nDCG@5 w.r.t. colpali-v1.1 trained on the same data ! 🚀 (1/N) huggingface.co/vidore/colqwen…
Want to dig deeper into ColPali? 👀 I’ve just dropped a series of cookbooks that covers similarity map generation and fine-tuning ColPali (with optional quantization). Find these crispy new recipes at github.com/tonywu71/colpa…! 👨🏻🍳 (1/N)
ColPali is revolutionizing multimodal retrieval, but could it be even more effective with domain-specific fine-tuning? Check out my latest blog post, where I guide you through creating a ColPali fine-tuning dataset using @Alibaba_Qwen's Qwen2-VL-7B-Instruct model to generate…
✨🎨🏰Super excited to share our new paper Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness Inspired by biology we 1) get adversarial robustness + interpretability for free, 2) turn classifiers into generators & 3) design attacks on vLLMs 1/12
@arjunaaqa @vespaengine I believe that this paragraph from the blog post answers that question
happy to announce BMX! BMX is an extension of BM25 to add entropy-weighted similarity and semantics. It outperforms BM5 on BEIR, BRIGHT, LoCo and some multilingual benchmarks. for BRIGHT it outperforms even 7b parameter embedding models with query augmentation. links 👇
The @kyutai_labs fully end-to-end audio model demo of today is a huge deal that many people missed in the room Mostly irrelevant are the facts that: - they come a few week after OpenAI ChatGPT-4o - the demo was less polished than the 4o one (in terms of voice quality, voice…
Since time immemorial, when a CEO asks a PM at Product Review, “what do you need to 10X users/revenue?”, “what will make you go faster?”, etc The PM steadfastly responds “We need [N] more engineers”. The Eng Mgr nods approvingly A story thread, with some hard truths to swallow:
Another fantastic writeup from @eugeneyan this time focusing on integrating LLMs into products and systems. As usual (see previous posts on for example on recsys/search) his ideas are elegantly visualized on a simple 2d graph. Nice one Eugene!
Another fantastic writeup from @eugeneyan this time focusing on integrating LLMs into products and systems. As usual (see previous posts on for example on recsys/search) his ideas are elegantly visualized on a simple 2d graph. Nice one Eugene!
When a new model is released, those are the key metrics I first look at to understand its performance: 👀 - MixEval: A dynamic benchmark evaluating LLMs using real-world user queries and benchmarks, achieving a 0.96 model ranking correlation with Chatbot Arena.…

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John Tejada @johntejada
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Varshitha @MLAIConference
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QuantumTrading🇺�... @Ouhalhawb03272
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RateCutTrader🇺🇸 @Altawwem625028
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Michael @MichaelLee04
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zach lieberman @zachlieberman
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vicki @vboykis
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🦋 shiffman.lol @shiffman
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Hamel Husain @HamelHusain
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Andrew Carr 🤸 @andrew_n_carr
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Yu Su (hiring postdoc... @ysu_nlp
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eugene @eugeneyalt
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DSPy @DSPyOSS
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soch @sochwellbeing
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