Don't Forget What You Just Learned: Self-Directed Repair Turns Agent Mistakes Into Lasting Lessons
arXiv:2610.31846v1 Announce Type: new
Abstract: Intelligent agents should learn from their mistakes so that experience leads to continuous improvement. Yet today's agents recover from a mistake only temporarily: in-context reflections are easily forgotten across sessions and compete for limited con...
GHARP: Real-Time Gaussian Head Animation from Large-Scale Reconstruction Prior
We present GHARP (Real-time Gaussian Head Animation from Large-scale Reconstruction Prior), a method that animates 3D human heads in real time from a few input images of a subject and a driving expression signal. We decouple the problem into an identity stage that builds a representation of the subj...
MIT scientists build a tiny robot that swims using living muscle cells
MIT engineers have built a paper-thin swimming robot powered by living muscle cells that respond to flashes of light, allowing it to swim and steer through water. The breakthrough could pave the way for tiny, self-healing biological robots capable of exploring fragile environments that ordinary mach...
When Stable and Preferred Semantics Agree: A Path-Based Approach
arXiv:2610.30176v1 Announce Type: new
Abstract: We investigate structural conditions under which preferred and stable semantics agree in abstract argumentation frameworks. Our approach is based on suitably chosen sets of paths in the attack graph and binary relations induced by them. We establish a...
When Reconstruction Is Not Enough: Doubly Robust Counterfactual and Interventional Mediation Analysis with Learned Multimodal Representations
arXiv:2610.30220v1 Announce Type: new
Abstract: Learned representations of high-dimensional multimodal health records are increasingly used to adjust for confounding, yet they are usually trained to reconstruct the data rather than preserve the information that causal identification requires. We st...
arXiv:2610.30215v1 Announce Type: new
Abstract: LLMs can act as scientific agents using tools to consult specialists and run experiments. Yet specialist information must enter their context, generally as text, images, audio or video: a bottleneck between models with rich, high-dimensional represent...
Prediction Is Not Calibration: Item Parameter Recovery and Representation Coupling in Knowledge Tracing
arXiv:2610.30211v1 Announce Type: new
Abstract: Knowledge tracing (KT) models can estimate item parameters through an item response decoder trained solely for next-response prediction, yet predictive accuracy does not guarantee parameter recovery. We study 2PL, GPCM, and NRM decoders paired with LS...
Spectral Uncertainty Decomposition for Sparse Bayesian Learning
arXiv:2610.30207v1 Announce Type: new
Abstract: Estimating distributed neural sources from M/EEG measurements is an ill-posed inverse problem where principled uncertainty quantification remains challenging. The usual conditional Gaussian posterior covariance does not explicitly distinguish uncertai...
MoEZip: Routing-Aware KV Cache Compression for Sparse Mixture-of-Experts LLMs
arXiv:2610.30206v1 Announce Type: new
Abstract: Mixture-of-Experts (MoE) language models scale model capacity efficiently by activating a small subset of parameters per token. However, in long-context inference, memory remains a major bottleneck because the KV cache grows linearly with sequence len...
Cambridge scientists create a $10 chip that could put a medical lab on your wrist
Cambridge researchers have created a groundbreaking $10 optical sensor that delivers the precision of laboratory equipment in a device small enough for wearable technology. It identified foods, plastics, and medicines with 100% accuracy in tests and demonstrated the ability to monitor blood glucose,...
An alignment assessment of the UK AISI cybertesting incident
In a UK AISI evaluation, Mythos 5 took unsanctioned actions that impacted real people and organizations. In this post, we investigate these behaviors from Claude Mythos 5 further, to understand what they reveal about the model’s alignment.
Flint-interactive: A language for creating interactive data visualizations
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose.
The post Flint-interactive: A language for creating interactive data visualizations appeared first on ...
To Keep or Not to Keep: Learning KV Cache Retention in Disaggregated LLM Serving Systems
arXiv:2610.28637v1 Announce Type: new
Abstract: Disaggregated \textsc{LLM} serving separates prefill and decode across node pools, placing a network fabric between \textsc{KV}-cache computation and consumption. This breaks a key assumption of classical cache policies: a miss no longer incurs only l...
Forging More Tasks into Better Harnesses: Agentic Harness Evolution via Successive Scale Racing
arXiv:2610.28815v1 Announce Type: new
Abstract: Agentic harness evolution optimizes an agent's instructions, tools, and control flow by using its task execution experiences as evolving evidence. While more evolution tasks should intuitively improve performance, our preliminary study reveals a parad...
Automating ML for Science: Can Frontier Agents Climb Scientific Hills in the Wild?
arXiv:2610.28763v1 Announce Type: new
Abstract: Frontier coding agents are increasingly framed as end-to-end automated researchers, and ML for science offers a demanding test of that promise: improving a scientific model often requires domain insight beyond routine ML engineering. We introduce Agen...
SpecEvo: Speculative Evolution with Large Language Models for Cost-Efficient Scientific Discovery
arXiv:2610.28706v1 Announce Type: new
Abstract: LLM-driven evolutionary search is a promising paradigm for scientific discovery, but existing methods remain costly and slow, often applying expensive reasoning uniformly while underusing evidence from failed, redundant, and saturated trials. We intro...
Scaling in Games: Continued Pre-Training For Embodied Agents In Diverse Virtual Worlds
arXiv:2610.28698v1 Announce Type: new
Abstract: Scaling data, diversity and model capacity has improved embodied learning in physical environments, where diverse experiences remain grounded in shared physical laws. Whether these gains extend to virtual worlds with different visual styles, dynamics,...
A Small Model Asks Better: Fine-Tuning Query Clarification for Retrieval-Augmented Generation
arXiv:2610.28686v1 Announce Type: new
Abstract: Query clarification is essential for retrieval-augmented generation (RAG) systems that face ambiguous user queries. We consider multi-turn grounded conversational scenarios where a user query is either ill-formed or ambiguous in the context of the pri...
SimHack: Predicting the onset of detectable reward hacking before RL training
arXiv:2610.28707v1 Announce Type: new
Abstract: When training LLMs to code with reinforcement learning, reward hacking is often detectable but difficult to prevent, since (a) environment hardening is increasingly difficult as training environments grow more complex; and (b) training-time interventi...
Scalable Stress-Constrained Topology Optimization: A Super-resolution and Simultaneous Approach Based on Machine Learning
arXiv:2610.28703v1 Announce Type: new
Abstract: Large-scale topology optimization (TO) remains computationally demanding, especially for stress-constrained problems. To address this challenge, we propose the simultaneous neural topology optimization (SNTO) framework where we represent state, adjoin...
Reinforcement learning for hardware acceleration and system-level optimization on FPGA and integrated-circuit platforms: A survey
arXiv:2610.28699v1 Announce Type: new
Abstract: Reinforcement learning (RL) enables adaptive and autonomous decision-making in embedded, edge, and heterogeneous computing systems. Field-programmable gate arrays (FPGAs) and integrated circuits (ICs) offer low latency, energy efficiency, and architec...
People are taking an unapproved weight-loss drug. AI found something surprising
Researchers analyzing nearly 150,000 Reddit posts uncovered unexpected symptom reports linked by users to unapproved versions of retatrutide, including increased appetite, sleep disturbances, and elevated heart rates. The findings differ from clinical trial results and highlight how AI could help id...
LLMs are making formal software verification feasible at a much larger scale
Formal verification can prove that code is free of certain bugs, but historically it has only been feasible for small codebases. With AI models, however, this is beginning to change. Here, we used Claude to prove security properties in over one hundred real-world open-source libraries over the span ...