The Kim Jaechul Graduate School of AI at KAIST
Seoul, Republic of Korea
Joined March 2022
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KAIST AI retweeted
ThinkWorld
World Imagination Reinforces Chain-of-Dynamics Reasoning
paper: huggingface.co/papers/2610.3…
KAIST AI retweeted
Introducing OmniShowcase: subject-to-video that shows every side of the subject from 4 images, even 360° orbits.
~2x lower geometric error than the best multi-view baseline on ShowcaseBench & real-world NAVI.
Project page: justin4ai.github.io/OmniShow…
Paper: arxiv.org/abs/2610.35684
#2 in the world! 🌏🔥
Huge shoutout to our amazing students, researchers, and our entire KAIST family! Let's keep pushing! 🚀
KAIST AI retweeted
🤖 What if robots could reason not just in words, but through the dynamics of the physical world?
🚀 Introducing 𝗧𝗵𝗶𝗻𝗸𝗪𝗼𝗿𝗹𝗱 from @NVIDIAAI !
💡 By reasoning through shared physical dynamics, robots could learn from human experience and transfer motion intelligence across embodiments.
𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀
→ 𝗖𝗵𝗮𝗶𝗻-𝗼𝗳-𝗗𝘆𝗻𝗮𝗺𝗶𝗰𝘀 (𝗖𝗼𝗗): A shared motion reasoning space across human and robot embodiments.
→ 𝗪𝗼𝗿𝗹𝗱 𝗜𝗺𝗮𝗴𝗶𝗻𝗮𝘁𝗶𝗼𝗻: Imagines future world representations conditioned on CoD.
→ 𝗥𝗟 𝗳𝗿𝗼𝗺 𝗪𝗼𝗿𝗹𝗱 𝗜𝗺𝗮𝗴𝗶𝗻𝗮𝘁𝗶𝗼𝗻: Reinforces reasoning through alignment between imagined and observed futures, enabling learning from human and robot videos.
𝗪𝗵𝗮𝘁 𝗶𝘁 𝗲𝗻𝗮𝗯𝗹𝗲𝘀
→ Robust real-world manipulation under unseen objects and environments.
→ Human-guided skill composition for novel multi-step robot tasks.
→ Robust generalization under task and environment shifts in simulation.
🌍 𝗢𝘂𝗿 𝘃𝗶𝘀𝗶𝗼𝗻: Scale generalizable motion intelligence through human videos toward a unified System-1 reasoning layer for Physical AI.
Paper, robot demos & more below! 👇
KAIST AI retweeted
🚨 New paper alert!
Despite recent advances in reward fine-tuning for diffusion and flow models, achieving high rewards remains challenging without sacrificing sample diversity or substantially drifting away from the pretrained distribution.
We address this with DFR: Distributional Functional Rewards, a framework that balances reward optimization with preserving desirable properties of the generated distribution! 🧵
KAIST AI retweeted
[QUESTION] Given a text, can we identify which AI model wrote it? This question is becoming increasingly important due to model distillation and misuse (e.g., Anthropic's www-cdn.anthropic.com/e50be2…).
[ANSWER] Our work (komseok0109.github.io/ATLAS/) @KAIST_AI shows that we can identify the source LLM family across different writing domains and model versions, and detect traces of teacher models after distillation.
🌐 Project page: huggingface.co/spaces/ming99…
🤗 Demo: huggingface.co/spaces/ming99…
🔗 LinkedIn: lnkd.in/p/gCaC4_iw
📜 arXiv: arxiv.org/abs/2610.19583
KAIST AI retweeted
🔬 Can AI discover unknown scientific knowledge by itself?
Excited to introduce Principia: Test-Time Hypotheses-to-Principle Construction for Scientific Discovery.
Principia uses LLMs to turn experimental observations into scientific principles at test time:
🔍 From observations to hypotheses
Compare related inputs and their outcomes to propose explanations for how specific characteristics contribute to the observed results.
🧠 From hypotheses to principles
Generalize recurring relationships across hypotheses into broader principles that guide subsequent predictions and candidate generation.
🔄 Refine knowledge with new observations
Use new observations to revisit earlier hypotheses and reconstruct the principles built on them.
Huge thanks to my co-authors at @KAIST_AI: @SangwooPark0306, @jinheonbaek, @SoyeongJeong97, and @SungJuHwang1
📄 Paper: arxiv.org/abs/2610.32195
#AIforScience #ScientificDiscovery #LLM #TestTimeLearning #KnowledgeConstruction
KAIST AI retweeted
🎉 Pivot-SD is an ORAL at #EMNLP2026!
We post-train a diffusion LM with a loss on just 10 tokens per response, and it beats both SFT and diffusion RL on math & code.
The trick isn't training on fewer tokens. It's training on the right ones.
EMNLP is right around the corner!
Meet our students who are excited to connect and meet new people at the conference! Take a look and say hello! 😁
kaist-gsai.github.io/emnlp20…
KAIST AI retweeted
🤖 Improving robot foundation models with real-world RL, then continually carrying that experience forward across tasks.
Introducing 𝗔𝗖𝗖𝗥𝗨𝗘: 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗮𝗹 𝗼𝗳𝗳-𝗽𝗼𝗹𝗶𝗰𝘆 𝗥𝗟 for robot foundation models.
Our real-world policies learn through physical robot interaction, starting from a small set of demos.
We inherit the actor–critic across tasks and distill earlier behavior, so experience accumulates while prior skills remain.
💻 𝗟𝗼𝘄 𝗮𝗱𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗚𝗣𝗨 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗼𝘃𝗲𝗿𝗵𝗲𝗮𝗱: RL updates a compact actor–critic while the foundation model stays frozen.
⚡ Subsequent tasks reach 95% success in about 𝟰–𝟭𝟮 𝗺𝗶𝗻𝘂𝘁𝗲𝘀 𝗼𝗳 𝗿𝗼𝗯𝗼𝘁 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻.
On real robots:
📉 Up to 68.5% less robot interaction vs. single-task RL
🎯 98.3% final prior-task success
🎬 A 60-second overview below.
🌐 accrue-rl.github.io/
🧵 1/6
KAIST AI retweeted
Do language models always need to see the full 1M-token context window? What if they can just tell us which tokens they actually need?
We're excited to share Declarative Attention (DA), a sparse attention protocol that lets a model declare which context they need for each reasoning step, in plain text. The inference engine can use this to mask out up to 99% of KV cache reads for global attention.
Across 15 long-context tasks, DA reduces global attention KV reads by 46% with only 1.4pp accuracy drop on Gemma-4 31B and 20% with 2.1pp on Qwen-3.6 27B. This accuracy gap shrinks as we scale up the model size. Zero-shot.
We discuss further potential in agentic settings with dedicated post-training, and model-controlled KV cache offloading as a path toward reversible context compaction.
We explain details and link our paper below.
KAIST AI retweeted
Can explicitly learning future point motion improve the model that generates those future images?
We introduce VPM (Video-Point Model), a 4D world model that jointly forecasts future multi-view videos + persistent 3D point trajectories.
Short answer: yes. Teaching a video world model to forecast 3D point tracks makes its videos more faithful & 3D-consistent, and makes robots better.
🌐 Project: rootyjeon.github.io/video-po…
🧵 [1/6]
Can AI agents learn from their own failures? 🤖
Excited to host Prof. @XiaoxiaoLi8 (@UBC, @VectorInst, @YaleMed) for an invited talk on how agents can diagnose failures, adapt, and recursively improve themselves, drawing lessons from vision and medical AI!
🤖 Made with AI
KAIST AI retweeted
📂 Agents are now taking over some of our knowledge work! But, after working through a stack of files over dozens of turns, something they saw slips out of what they deliver.
Excited to introduce RunningTab🧾, where the environment keeps a running tab of what the agent has read and what the task still owes, until the work is handed in.
From writing reports and checking budgets to pulling figures from a century of government records, it consistently outperforms the same agent without the tab and baselines that track the task in the model itself.
Paper: huggingface.co/papers/2610.1…
KAIST AI retweeted
Introducing SQAM, Q-Learning with Scalar Adjoint Matching! 🚀 Off-policy RL for flow policies, without a single VJP through the policy. Bigger gains on the hardest tasks, at a lower cost per update. 🧵 [1/8]
KAIST AI retweeted
Full-duplex dialogue models listen and speak at the same time, yet are still evaluated against a partner that cannot react.
✨ Meet DyaFDB: two full-duplex models converse directly, and we score both.
📄 Paper: arxiv.org/abs/2610.08125
🌐 Page: dyafdb.github.io/
1/ 🧵
KAIST AI retweeted
Speech+RL paper from @Qualcomm AI Research and @KAIST_AI !
For more natural conversational AI, we usually use full-duplex speech language models, which can perform smooth turn-taking and also produce uh-huh-like backchannels.
However, there is no clear answer to when a model should barge in. So in this field, we explore reinforcement learning rather than relying solely on supervised fine-tuning. But due to the nature of full-duplex SLMs, their token distribution is totally different from that of normal language models. So our idea is to build a reinforcement learning method for full-duplex speech language models. Our method, HiPLEX (Hierarchical policy factorization for full-duPLEX SLMs), factorizes the policy into when to talk and what to talk. Plus, our credit assignment method causally attributes rewards to the correct or incorrect timing and semantic decisions. (See Figure 1 featuring the cute San character!)
arxiv: arxiv.org/abs/2610.07727
huggingface paper: huggingface.co/papers/2610.0…
Huge thanks to my Qualcomm speech team.
KAIST AI retweeted
Speech+Agent paper from @Qualcomm and @KAIST_AI
When we handle a cascaded speech agent system (ASR+LLM+TTS) for any reason, the LLM can call tools, but latency is a problem. We researched how we can hide the tool-calling latency and found that we can hide it within ASR time.
We tested it on the Samsung Galaxy S26 Ultra and confirmed that we can reduce latency and also (which we didn’t anticipate) improve performance!
Check out our paper!
arxiv: arxiv.org/abs/2610.07641
🤗 : huggingface.co/papers/2610.0…
LinkedIn: lnkd.in/p/gSvihjfE
