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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 4
Towards Generalist Graph-Level Anomaly Detection
Graph-level anomaly detection (GLAD) aims to identify graphs that deviate significantly from the majority.CAMP: Context-Aware Mapping and Placement for LLMs on 3D-IMC Arrays
Large Language Models (LLMs) incur severe memory-access overheads due to the cost of loading large weight matrices and key-value (KV) caches.Pediatric ECG foundation model via adult-to-pediatric fine-tuning: downstream tasks and clinical evaluation
Pediatric electrocardiogram (ECG) interpretation is complicated by age-dependent electrophysiological changes that limit direct transfer of adult AI models.Evaluating Language Model Features for Zero-Shot Time-Series Anomaly Detection Under Isolation Assumption
Time-Series Anomaly Detection (TSAD) is a fundamentally challenging task: it is unsupervised, requires high local precision capabilities, and suffers from limited labelled data.Continued Training of Continuous Language Flows with Token Identity Squared Supervision
Diffusion and flow models have recently been extended to language modeling, generating text through continuous denoising in token embedding spaces.Seeing Beyond the Next Step: World-Model-Guided Human-Like Navigation in Multi-Agent Scenes
Selecting a next motion in a populated scene is not just a matter of avoiding what is currently in the way: an action can be locally feasible yet drift onto the road instead of the crosswalk, cut…O$^2$PD: Overlap-Gated On-Policy Distillation on Reliable Prefix
On-policy distillation (OPD) supplies dense token-level supervision by aligning a student to the teacher's next-token distribution on its own generated rollouts.Evaluating Token Probabilities as Confidence Estimators in Human Behavior Simulations: A Case Study in Luxembourgish Next Like Prediction
As large language models (LLMs) are increasingly used to simulate human behavior, their operational validity is typically assessed through accuracy alone, implicitly treating human behavior as…Extending Reasoning in Typed Decision Models via Looping
Typed decision models choose among a fixed set of answer options in a single forward pass, so unlike generative language models they cannot spend more test-time compute on hard inputs by generating…Scaling Laws for SAE Training Data
Activation storage is often a bottleneck in sparse autoencoder (SAE) training.RIVER: Evidence-guided multi-agent retrieval for enzyme--reaction discovery
Enzyme function annotation and the identification of candidate enzymes for a given reaction are essential steps in connecting genomic information to biochemical reaction networks.The Intrusion Pathway: How Non-Evidential Context Degrades Language Model Performance
Language models lose accuracy when a prompt contains non-evidential context: irrelevant statements from unrelated domains, or random off-distribution tokens, with no competing answer and no semantic…Bridging Structure and Semantics: Knowledge Graph Verification via Triple-to-Text Alignment
Semantic accuracy is an important dimension of knowledge graph quality.Axis-LoRA: Probing and Adapting Embedding Models for Personal-Memory Retrieval
As LLMs are increasingly asked questions about a user's personal experiences, retrieval must distinguish memories by time, place, person, and event.Generate-then-Rank: Learning Symbolic Goal Interpretation for Embodied Agents
Goal interpretation is to translate a natural language instruction into a formal symbolic goal that a planner can execute.Instantiating the Knowledge Plane
In 2003, Clark et al. proposed the "Knowledge Plane", a layer of intelligent agents that share rich information about network state in order to collaboratively manage the Internet across…STAG-Net: Structure-Aware Attention over Landmark Groups for Lightweight Head Pose Estimation
Head pose estimation is important for human computer interaction and driver monitoring, but many image based methods remain too costly for resource constrained deployment.THG-Omni: Traceable Human-Centric Graph Supervision for Video Reasoning
Omni-modal models can answer questions about video, but their predictions rarely show which participant, event, or interval supports an answer.Masked diffusion as random sequential adsorption: an exactly solvable model for parallel decoding
Masked diffusion models generate text by reversing a stochastic masking process.Computation, not weight access: large language models as auditors of clinical prediction networks
Frontier large language models (LLMs) have been reported to read, edit and write the weights of small neural networks, raising the prospect of auditing clinical prediction models from their files…