Berkeley AI Research retweeted
1/🧵 Coding agents can make code faster. Can they also prove it still does the same thing?
Introducing Agentic Verified Optimization 🚀 Agents return faster code plus a Lean proof that it equals the original on every input.
🌐 Blog: aqueduct.verina.io
📄 Paper: arxiv.org/abs/2610.30188
Introducing Agentic Verified Optimization 🚀 Agents return faster code plus a Lean proof that it equals the original on every input.
🌐 Blog: aqueduct.verina.io
📄 Paper: arxiv.org/abs/2610.30188
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Berkeley AI Research retweeted
We trained an imaginative agent! ✨
Agima: an omni agent that plans by imagining futures.
𝗦𝗙𝗧: Directly generalizes to harder tasks! 𝟮%→𝟰𝟬%, beats GPT-5.6 Sol (𝟮𝟬%).
𝗥𝗟: New ways to imagine emerge! Imagines a second plan only if the first one fails (𝟯𝟮%→𝟭𝟬𝟬%).
Agima: an omni agent that plans by imagining futures.
𝗦𝗙𝗧: Directly generalizes to harder tasks! 𝟮%→𝟰𝟬%, beats GPT-5.6 Sol (𝟮𝟬%).
𝗥𝗟: New ways to imagine emerge! Imagines a second plan only if the first one fails (𝟯𝟮%→𝟭𝟬𝟬%).
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Berkeley AI Research retweeted
Leveraging research papers to push the frontier of the LLM evaluation!
FrontierCS 2 is our new benchmark for CS research & development.
Each task is based on a paper: we delete the contribution from the released code, and ask agents to outdo the original method, as judged by the authors' chosen metrics. Agents don't have access to the paper or web.
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Berkeley AI Research retweeted
FrontierCS 2 is our new benchmark for CS research & development.
Each task is based on a paper: we delete the contribution from the released code, and ask agents to outdo the original method, as judged by the authors' chosen metrics. Agents don't have access to the paper or web.
Each task is based on a paper: we delete the contribution from the released code, and ask agents to outdo the original method, as judged by the authors' chosen metrics. Agents don't have access to the paper or web.
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Berkeley AI Research retweeted
1/ 💰Multi-vector retrieval shouldn’t charge you by the token!
🎉 Excited to share FLINT (Fixed-budget Late INTeraction), a late-interaction retriever designed for long, complex queries with up to 89% reduction in index and 23-170× faster retrieval latency.⚡
🎛️ FLINT gives a alance storage, latency, and quality.
📄 Paper: arxiv.org/abs/2610.27273
How does it work? 🧵
ColBERT stores and compares one vector per token. FLINT pools every query and every document into a fixed number of vectors, so there’s far less to store and far fewer comparisons at search time.
🎉 Excited to share FLINT (Fixed-budget Late INTeraction), a late-interaction retriever designed for long, complex queries with up to 89% reduction in index and 23-170× faster retrieval latency.⚡
🎛️ FLINT gives a alance storage, latency, and quality.
📄 Paper: arxiv.org/abs/2610.27273
How does it work? 🧵
ColBERT stores and compares one vector per token. FLINT pools every query and every document into a fixed number of vectors, so there’s far less to store and far fewer comparisons at search time.
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Berkeley AI Research retweeted
My commentary for @Nature on this nice study - nature.com/articles/s4158…. - that develops and tests a personal AI health agent.
Accompanying @NatureNV pointing out the next big step would be to do a RCT for health outcomes nature.com/articles/d4158… by @2plus2make5
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Berkeley AI Research retweeted
Excited to share TokenGate. By teaching LLMs to conditionally skim context tokens, we achieve up to 11.3x faster prefill and drastically reduce peak memory for long-context tasks!
Introducing ✨Token-Gated Language Models✨ for up to 11x faster prefill by learning to skim context, conditionally forwarding only tokens that matter.
Akin to the shift from dense models to MoEs, the next frontier of efficient LLMs is conditional computation over a new scaling axis: context tokens🧵👇
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Berkeley AI Research retweeted
Great work on fast long-context prefill. TokenGate is a trainable module that works with existing models to make long-context inference much more efficient.
Introducing ✨Token-Gated Language Models✨ for up to 11x faster prefill by learning to skim context, conditionally forwarding only tokens that matter.
Akin to the shift from dense models to MoEs, the next frontier of efficient LLMs is conditional computation over a new scaling axis: context tokens🧵👇
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Berkeley AI Research retweeted
Game changer for humanoid full-body control training. Now it's superfast (and cheap, just one GPU) to train a single policy on *all* the motions. How come:
(1) World model much faster than regular sim
(2) Backprop through time collects way more signal than policy gradients
(1) World model much faster than regular sim
(2) Backprop through time collects way more signal than policy gradients
We found a way to scale humanoid control that reduces GPU-hours by more than 1000×.
Across 129,514 motions, SuperMimic surpasses the state of the art (128 GPUs, 7 days) with 1 GPU in 3 hours.
Project: supermimic.github.io
Demo: supermimic.github.io/demo/
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Berkeley AI Research retweeted
We found a way to scale humanoid control that reduces GPU-hours by more than 1000×.
Across 129,514 motions, SuperMimic surpasses the state of the art (128 GPUs, 7 days) with 1 GPU in 3 hours.
Project: supermimic.github.io
Demo: supermimic.github.io/demo/
Across 129,514 motions, SuperMimic surpasses the state of the art (128 GPUs, 7 days) with 1 GPU in 3 hours.
Project: supermimic.github.io
Demo: supermimic.github.io/demo/
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Berkeley AI Research retweeted
Estimating empowerment from offline data is useful, because it provides us with an unsupervised objective for learning the relative "value" of states for reaching diverse outcomes. But it's notoriously hard to make this tractable. In our new paper, we present a tractable estimator, as well as some applications!
How should an agent decide where to explore, given unlabeled offline data? 🤖
In our new paper, we show that empowerment, a measure of how much control an agent has at a state, can be estimated from offline data and used to guide exploration!
ishirgarg.github.io/offline-empowe…
(1/7)
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Berkeley AI Research retweeted
How should an agent decide where to explore, given unlabeled offline data? 🤖
In our new paper, we show that empowerment, a measure of how much control an agent has at a state, can be estimated from offline data and used to guide exploration!
ishirgarg.github.io/offline-empowe…
(1/7)
In our new paper, we show that empowerment, a measure of how much control an agent has at a state, can be estimated from offline data and used to guide exploration!
ishirgarg.github.io/offline-empowe…
(1/7)
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Berkeley AI Research retweeted
Turns out that simply extracting policies with supervised learning conditioned on the advantage can get state of the art results in RL, as long as we get a few important details right!
Last year I trained the advantage-conditioned VLA for π*0.6 during my internship @physical_int. Now, I have a much improved recipe.
Introducing ✨Advantage Guidance✨, a performant RL method with stable advantage-conditioned supervised training and contrastive guidance sampling
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Berkeley AI Research retweeted
Follow Gemini 4 Argon's report, I implemented their robotics harness.
Gemini did a zero-shot bimanual handover. Now open source 🤖👇
Gemini did a zero-shot bimanual handover. Now open source 🤖👇
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Berkeley AI Research retweeted
❓How can humanoids learn by watching humans?
Naive retargeting can lose crucial contacts: like bracing a hand on a table while bending to pick an object off the floor.
🚀 Introducing VICAR: preserve key contacts from human videos, practice in simulation, deploy on hardware
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Naive retargeting can lose crucial contacts: like bracing a hand on a table while bending to pick an object off the floor.
🚀 Introducing VICAR: preserve key contacts from human videos, practice in simulation, deploy on hardware
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Berkeley AI Research retweeted
New surprising result:
We trained AI recursively on its own safe generations, while giving it a benign cue. Models became increasingly misaligned over time, giving advice on how to do harm and build biological weapons.
We call this Progressive Misalignment. 🧵👇
We trained AI recursively on its own safe generations, while giving it a benign cue. Models became increasingly misaligned over time, giving advice on how to do harm and build biological weapons.
We call this Progressive Misalignment. 🧵👇
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Berkeley AI Research retweeted
Last year I trained the advantage-conditioned VLA for π*0.6 during my internship @physical_int. Now, I have a much improved recipe.
Introducing ✨Advantage Guidance✨, a performant RL method with stable advantage-conditioned supervised training and contrastive guidance sampling
Introducing ✨Advantage Guidance✨, a performant RL method with stable advantage-conditioned supervised training and contrastive guidance sampling
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Berkeley AI Research retweeted
Seeing is an act in itself. We look at what matters, and so should robots!
EyeRobot 2.0 does this with gaze, enabling precise manipulation without wrist cameras. Key for tool use, where the tool can block a wrist camera!
Co-led by @justkerrding who'll be on the job market! Hire him :)
EyeRobot 2.0 does this with gaze, enabling precise manipulation without wrist cameras. Key for tool use, where the tool can block a wrist camera!
Co-led by @justkerrding who'll be on the job market! Hire him :)
Almost all bimanual robots include wrist cameras. They add bulk, cost, and complexity, but are critical for performance. Are they really needed?
Excited to finally share EyeRobot 2.0, where we use active gaze from an ego stereo camera instead 🧵 (with @KushtimusPrime)
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Berkeley AI Research retweeted
1/ We discovered something totally unexpected about the neural structure of consciousness that overturns decades of thinking on its biological basis and brings us significantly closer to the recipe for generating conscious awareness.
New preprint: biorxiv.org/content/10.648… (led by @HesseJanis, with @FrankLanfranchi, @_conor_mcgrory, @Yuelin_Shi, and @EngelTatiana)
New preprint: biorxiv.org/content/10.648… (led by @HesseJanis, with @FrankLanfranchi, @_conor_mcgrory, @Yuelin_Shi, and @EngelTatiana)
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Berkeley AI Research retweeted
Excited to be co-organizing our Trust4RSI-Agent Workshop at #AAAI2027 ! A Workshop on Trustworthy Evolution of AI Agents! 🚀
🥳We have an amazing lineup of speakers and panelists: @Yoshua_Bengio , @MengdiWang10 , @du_yilun , @hugo_larochelle , @ZheqingZhu , and @__yuezhao__ e: November 20, 2026
📬 Author notification: December 2, 2026
📍 February 22–23, 2027 · Montréal, Canada
🌐 trust4rsi-agent.github.io/AAAI2027/
Please share with colleagues working in this space and looking forward to your submissions!
Happy to be organizing this alongside @Haolun_Wu0203 @HuaWenyue31539 @BangL93 @lcharlin! #AAAI2027 #AIAgents
🥳We have an amazing lineup of speakers and panelists: @Yoshua_Bengio , @MengdiWang10 , @du_yilun , @hugo_larochelle , @ZheqingZhu , and @__yuezhao__ e: November 20, 2026
📬 Author notification: December 2, 2026
📍 February 22–23, 2027 · Montréal, Canada
🌐 trust4rsi-agent.github.io/AAAI2027/
Please share with colleagues working in this space and looking forward to your submissions!
Happy to be organizing this alongside @Haolun_Wu0203 @HuaWenyue31539 @BangL93 @lcharlin! #AAAI2027 #AIAgents
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