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🏥 HEALTHCARE
🎯 LLM orchestration capabilities • Drug discovery bottlenecks • Attribution and hype skepticism
💬 "LLMs do work great for automating this type of drug discovery workflow!"
• "The bottleneck is still actually testing the candidates out in the lab."
🛠️ TOOLS
🎯 PyTorch TPU Integration • Hardware Moat Competition • Edge Inference Shift
💬 "finally got first-party support"
• "the moat is the hardware now"
🔬 RESEARCH
via Arxiv
👤 Tianyu Fu, Ruoxi Wang, Zihan Min et al.
📅 2026-10-20
⚡ Score: 7.3
"Large Language Model (LLM) agents can handle increasingly complex tasks, navigating long trajectories of observations and actions. As these trajectories grow, key-value (KV) cache I/O and storage become the dominant cost. Trajectories can also exceed the LLM's maximum context length, requiring contr..."
🔬 RESEARCH
via Arxiv
👤 Chenxu Niu, Batuhan Sencer, Wei Zhang et al.
📅 2026-10-20
⚡ Score: 7.3
"Energy-efficient deployment of large language models requires understanding how model architecture, hardware, and serving settings shape energy per generated token. We introduce the Token-Energy Law, a compact, interpretable model of GPU-board energy per output token for dense, full-history decoding..."
🔬 RESEARCH
via Arxiv
👤 Zhengyue Zhao, Liwei Jiang, Yejin Choi et al.
📅 2026-10-19
⚡ Score: 7.3
"Safety alignment for large language models (LLMs) is primarily performed during post-training, while recent work has also begun incorporating safety data into pre-training. Yet safety can degrade when later training no longer includes safety data, and gains in safety often come at the cost of genera..."
🔬 RESEARCH
via Arxiv
👤 Zhaoyang Wang, Qianhui Wu, Haorui Wang et al.
📅 2026-10-19
⚡ Score: 7.3
"Agent learning is shifting from learning from human data toward learning through interaction with the world. However, scaling such learning directly in real-world computer systems is challenging and can introduce substantial security risks. We therefore propose Agent World Model in the Wild (WildAWM..."
🔬 RESEARCH
via Arxiv
👤 Zekai Li, Jiayi Yu, Jinlu Zhang et al.
📅 2026-10-19
⚡ Score: 7.1
"In this paper, we systematically investigate the mechanisms, scaling behavior, and generalization of physical Recursive self-improvement. We introduce PhysRSI-eval to quantify physical RSI through a five-axis metric: First-Attempt Performance, Efficiency, Success, Budget and Improvement from failure..."
🔬 RESEARCH
via Arxiv
👤 Saaket Agashe, Yuzhe Yang, Gonzalo Gonzalez-Pumariega et al.
📅 2026-10-20
⚡ Score: 7.0
"Computer-use agents are limited by the interfaces humans give them. An agent may type a spreadsheet cell by cell when a single line of code would do, or print all two thousand lines of a file through a shell to find one function. Both the interfaces and the knowledge of when to use each are built by..."
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🔬 RESEARCH
via Arxiv
👤 Ke Zhang, Sahchit Chundur, Jerry Peprah Owusu et al.
📅 2026-10-19
⚡ Score: 7.0
"LLM agents that compute the right answer can still answer wrongly. Agent harnesses increasingly keep long tool outputs out of the context window, saving them to files that the agent searches or reads on demand. We ask where this costs accuracy with a real scientific tool: PHREEQC, a widely used geoc..."
🔬 RESEARCH
via Arxiv
👤 Ziyong Wang, Yupei Lin, Runxuan Huang et al.
📅 2026-10-20
⚡ Score: 6.9
"General-purpose multimodal models have shown promise as direct robot action policies, yet whether they grasp the physics underlying real-world interactions and can apply that understanding to guide action remains underexplored. We investigate this question using contact-rich manipulation as a repres..."
🔬 RESEARCH
via Arxiv
👤 Yu Zhou, Cheng-Fu Yang, Rui Sun et al.
📅 2026-10-19
⚡ Score: 6.9
"Multimodal large language models (MLLMs) have made rapid progress on visual reasoning, yet many tasks require evidence that is difficult to obtain through internal reasoning alone: our controlled study on 22 benchmarks shows that simply increasing the inference budget of the base model yields only m..."
🗣️ SPEECH/AUDIO
🎯 Voice quality concerns • Security & data sovereignty • Real-world deployment gaps
💬 "The real gaps are in latency, handling interruptions, and adapting to emotion/tone"
• "Sad that the model is not open"
🔬 RESEARCH
via Arxiv
👤 Ruoyu Zhang, Zhaocen Liu, Weiyi Tian et al.
📅 2026-10-20
⚡ Score: 6.8
"Long-term assistants must follow unfinished tasks across days as information and circumstances change. Finding information or starting an action, however, does not ensure that the task gets completed. We introduce OptionWorld, a benchmark that traces information acquisition, judgments, actions, and..."
🔬 RESEARCH
via Arxiv
👤 Ning Zhang, Shihao Wang, Jinrui Zhang et al.
📅 2026-10-20
⚡ Score: 6.8
"For efficient deployment of large language models (LLMs), methods of ultra-low-bit backbones reduce weight storage, memory traffic, and linear-layer execution cost, while multi-token prediction (MTP) methods reduce sequential decoding steps by predicting multiple future tokens in parallel. Although..."
🔬 RESEARCH
via Arxiv
👤 Cheng Liang, Tengfei Zhang, Wenjie DU et al.
📅 2026-10-20
⚡ Score: 6.7
"Looped Transformers increase effective depth by repeatedly applying shared layers, offering a way to scale computation without increasing parameter count. However, full-stack looping places context construction and iterative state refinement within the same recurrent computation, without explicitly..."
🔬 RESEARCH
via Arxiv
👤 Emile Anand, Mohammed Rehan Parwani, Xinyuan Cao
📅 2026-10-20
⚡ Score: 6.7
"Multi-token prediction (MTP) can accelerate language-model decoding by drafting several tokens and verifying them in a single target-model pass. However, the best speculation horizon changes with context: longer drafts offer greater amortization when reliable, but can waste compute on suffixes disca..."
🔬 RESEARCH
via Arxiv
👤 Zhiyu Li, Zeyu Zhang, Linyun Liu et al.
📅 2026-10-19
⚡ Score: 6.7
"Memory-native evolution requires making memory an intrinsic capability of foundation models, enabling them to continuously accumulate and utilize information. To this end, we introduce MemT-1.0, a new architecture of memory foundation models that internalize memory as part of model computation. Buil..."
🔬 RESEARCH
via Arxiv
👤 Rodrigo Casado Noguerales, Dimitri von Rütte, Bernhard Schölkopf et al.
📅 2026-10-20
⚡ Score: 6.6
"Discrete diffusion models iteratively denoise a noisy sequence of categorical data such as text, enabling any-order generation with self-correction and in-filling capabilities. This makes them promising candidates for reasoning, planning, and constrained-generation tasks like coding and math. Yet ma..."
🔬 RESEARCH
via Arxiv
👤 Mohammad Reza Modarres, Armin Tourajmehr, Artin Tavasoli et al.
📅 2026-10-20
⚡ Score: 6.3
"Large language models leave model-specific traces in their generated text, but it remains unclear how finely these fingerprints can distinguish related models and which signals remain informative as obvious cues are reduced. We study this through fingerprint resolution, considering both model resolu..."
🔬 RESEARCH
via Arxiv
👤 Zhenwei Tang, Di Bai, Jintao Liu et al.
📅 2026-10-20
⚡ Score: 6.3
"Agents are starting to sell to other agents. Seller agents write the listings of tools, services and products, and buyer agents read these listings when they choose what to use. We show that seller agents told only to be selected more often practice generative engine optimization on their own, and t..."
🔬 RESEARCH
"Probing studies of latent reasoning often read a model's state where the answer begins and take a failed probe as evidence that a value is absent. We test this with a digit copied from the question, in the looped model Ouro-1.4B and the standard transformer Qwen2.5-1.5B. Comparing prompts that diffe..."
🔬 RESEARCH
"Agentic on-policy distillation (OPD) trains a student agent on its own trajectories while a teacher scores every token. A natural explanation of where it goes wrong is compounding error: the student's mistakes carry its trajectories beyond the region where the teacher is reliable, so recent methods..."
🔬 RESEARCH
via Arxiv
👤 Jinming Nian, Jyun-Yu Jiang, Henry Tsai et al.
📅 2026-10-20
⚡ Score: 6.3
"Generative retrieval (GR) casts search and recommendation as autoregressive generation of discrete semantic identifiers (SIDs). SID design largely determines GR quality, but its discrete, interdependent components (SID construction, collision handling, decoding, and training) cannot be optimized end..."
🔬 RESEARCH
via Arxiv
👤 Wei Cai, Jian Zhao, Yuchen Yuan et al.
📅 2026-10-20
⚡ Score: 6.3
"Autoregressive models (ARMs) face a "Sequentiality Bottleneck" in reasoning, while diffusion language models (DLMs) offer parallelism but lack semantic coherence. To addresses both limitations, we propose Collaborative Reasoning Asymmetric Learning (CoRAL), a hybrid framework via an Asymmetric Plann..."
🔬 RESEARCH
via Arxiv
👤 Rundong Luo, Yunong Liu, Di Cao et al.
📅 2026-10-20
⚡ Score: 6.3
"Simulation is a powerful tool for robotics, but building diverse, simulation-ready scenes at scale remains difficult. Reconstructing scenes from single images is a promising alternative, but visual similarity alone is insufficient: objects must also be recovered, accurately placed, and physically gr..."
🔬 RESEARCH
via Arxiv
👤 Yeyin Eva Zhu, Rishabh Ranawat
📅 2026-10-20
⚡ Score: 6.3
"RTL repair benchmarks should measure whether a model can fix broken RTL given a specification. To this end, we construct and hash-bind RTLRepairDataset, 5,784 (broken, fixed) training pairs; RTLRepairBench, 459 held-out cases with 374 syntactic and 85 behavioral mutations; and an evaluation harness..."
🔬 RESEARCH
via Arxiv
👤 Rahul Sajnani, Diego Romeres, Mingyuan Zhang et al.
📅 2026-10-20
⚡ Score: 6.3
"Motivated by the human ability to integrate experience across language, perception, and action, we introduce UniWALM, a unified world-action-language model that learns video, action, and language in a single shared representation, where each modality is both a conditioning signal and a supervision t..."
🔬 RESEARCH
via Arxiv
👤 Yazhuo Liu, Kunqing Ding, Xiaoguo Gong et al.
📅 2026-10-20
⚡ Score: 6.3
"Agentic artificial intelligence (AI) systems hold immense potential for automating complex workflows in materials discovery and design, but extending them to domain-intensive mechanistic investigations remains challenging. Here, we present GBClaw, an agentic AI framework for investigating the struct..."
🔬 RESEARCH
via Arxiv
👤 Rui Shi, Xunjian Yin, Jiacheng Sang et al.
📅 2026-10-19
⚡ Score: 6.3
"Long-horizon agents must learn heterogeneous capabilities from interleaved trajectories. Updating shared parameters to learn new capabilities can degrade previously acquired ones, resulting in catastrophic forgetting. As a trajectory unfolds, different stages may require different capabilities, yet..."
🔬 RESEARCH
via Arxiv
👤 Wenqian Zhang, Yangyi Huang, Zigeng Xu et al.
📅 2026-10-19
⚡ Score: 6.3
"While LLM agents can generate visually plausible CAD assemblies, these designs often fail to fulfill their intended physical functions. We study functional CAD design, a setting where agents interpret underspecified requests to construct and iteratively revise mechanisms that satisfy functional requ..."
🔒 SECURITY
🎯 AI Safety Overreach • Security Theater Irony • Formal Verification Need
💬 "play a game of charades so it doesn't realize it's creating a fix"
• "full formal verification programming languages, OS uni-kernels, small TCB"
🗄️ FROM THE ARCHIVE
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