AI & Machine Learning · Computer Science & AI
Artificial Intelligence
The newest papers in Artificial Intelligence that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
The newest 5,000 papers, from 11 October 2026 to 5 November 2026: 4,993 arXiv preprints and 7 OpenAlex records.
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Newest papers, page 25
Workload-Static Optical Circuit Switching For Scalable LLM Inference Decode
LLM inference decode executes fine-grained collectives whose latency directly limits per-user interactivity, yet scale-up fabrics are still built from electronic packet switches designed for training.From AutoResearch to Continual AutoResearch: Do Agents Learn Across Tasks?
Autoresearch has shown that agents can autonomously write code, run experiments, and iteratively improve machine learning solutions.The Right Source Is Not Enough: Understanding History Interference in Speech-LLM Memory
Speech large language models retain audio history to support multi-turn conversations.From Opinions to Commitments: Unstable Assignments and Silent Choices in AI-Mediated Proposals
Large language models (LLMs) are increasingly being used to turn citizens' opinions into collective proposals.Meta$^2$: Precomputing Reusable Knowledge for Online Harness Evolution in Long-Horizon Agents
Harness optimization improves long-horizon agents by refining the prompts, tools, memory, and hooks around a frozen model.Creating Power Flexibility in Datacenters via Query Routing
The growing energy requirements of large language model inference highlight the need for datacenters to support safe grid operation by providing power demand flexibility, while maintaining service…Catalyzing Interactive HTML Generation Capabilities via High-Fidelity Reinforcement Learning
Generating HTML from natural-language specifications is an important application of code large language models (LLMs).Asynchronous Robot Policies
Scaling vision-language foundation models has significantly advanced generalist robot manipulation, but the increased model scale introduces computational latency that degrades real-time physical…Synthesizing Dexterous Robot Demonstrations from Human Videos via Virtual Try-On
Robot foundation models have shown remarkable performance in robot control, yet adapting them to a new task still relies heavily on teleoperated demonstrations.Basin2Vec: Learning Basin-Centric Embeddings for Scalable Hydrological Modeling
Reliable streamflow prediction across time and in ungauged basins requires representations that capture hydrologically relevant basin characteristics.Learning Behaviorally Relevant Dynamics in Multimodal Neural Recordings
Multimodal neural data can enable a more complete understanding of brain dynamics underlying behavior.AgentAM: An AI Agent for Spatter- and Defect-Aware Process Optimization in Laser Powder Bed Fusion
Predictive models for laser powder bed fusion (LPBF) are rarely used on the shop floor because querying them requires programming and model-specific knowledge.OperAItor: An Agentic Framework for Fault-Tolerant Operation of Chemical Processes
OperAItor, an open-source framework for developing and evaluating large language model agents for supervisory fault management in chemical processes.Fairness Metrics based on the AUC in Clinical Predictive AI
Fairness evaluation of clinical prediction models for binary outcomes often involves fairness metrics that compare the area under the receiver operating characteristic curve (AUC, or c-statistic)…Going Deeper With Memories
Byte level language models avoid tokenizer dependence and provide universal coverage, but processing every byte at full resolution is expensive.JHiMA-WebRL: A Joint Hierarchical Multi-Agent Reinforcement Learning Framework for Long-Horizon Web Tasks
Multimodal web agents have shown promising capabilities for interacting with websites through graphical user interfaces (GUIs), yet realistic web tasks often require long-horizon, multi-step…The Geometry of Ignorance: Detecting Suppressed Knowledge in LLMs through Activation Fragmentation
Evaluating whether a large language model (LLM) genuinely knows a concept remains a fundamental problem, since behavioral metrics can be fooled by suppression: a model that refuses to answer may…Beyond Accuracy: What Language-Subspace Interventions Capture, Remove, and Improve
Language-subspace interventions aim to improve multilingual reasoning by suppressing language-related components in mid-layer activations.Knowingly Wrong: Training LLMs Against Their Own Beliefs can Induce Misalignment
Fine-tuning a large language model (LLM) on harmful data in a narrow domain, such as bad medical advice, can make it broadly misaligned.False Reassurance in LLM Severe-Weather Guidance: A Replayable Evaluation Against Real NWS Alerts
An LLM assistant that cannot see official severe-weather alerts may tell users that conditions are calm while a warning is active.