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Artificial Intelligence
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The newest 5,000 papers, from 12 October 2026 to 6 November 2026: 4,992 arXiv preprints and 8 OpenAlex records.
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Newest papers, page 240
Choosing LLMs for Strategic Agent Interactions
Game-theoretic benchmarks claim that large language model (LLM) strategic decision-making varies across game types, interaction structures, and capability dimensions.ABot-ER: Unified Embodied Reasoning through Agentic Data-Model Co-Evolution
We introduce ABot Embodied Reasoning (ABot-ER), a unified embodied reasoning foundation model that breaks the tension between breadth and depth in embodied reasoning.Urbex: Agentic Spatial Grounding for City-Scale 3D Scenes
City-scale 3D grounding is commonly formulated as candidate ranking, which requires precomputed instance proposals and becomes inefficient in large outdoor scenes.Delegating Earth science model calibration to knowledge-guided LLM agents
Applying process-based Earth science models to new sites and questions often requires expert calibration decisions.PRISM: Calibrating Black-Box Judges with Counterfactual Probes
Large language models are increasingly used as judges to reduce reliance on costly human evaluation, yet their scores can systematically disagree with human judgments.Graph-free Retrieval with Graph-Modified Embeddings
Dense vector retrieval can miss evidence connected through intermediate entities, while direct graph retrieval requires access to the graph at query time.Not All Drift Is a Mechanism: Tracing Prompt Adaptation in LLMs
Prompt-based adaptation offers several ways to specialize a frozen language model, ranging from natural-language prompt optimization to learned continuous prompts.Do Factuality Methods Make LLMs More Factual? Reproducibility and Fair Comparison
Large Language Models (LLMs) are aligned to human preferences, after pre-training and supervised fine-tuning steps.Resource-Efficient 3D Shape and Orientation Estimation of Road Users using Roadside RGB Camera and 3D Radar
End-to-end deep learning pipelines for camera-radar 3D object detection are well established in driver assistance systems and autonomous vehicles; however, adapting them to roadside-mounted…Agaram 2.5B: The First Tamil-First Language Model Trained from Scratch with Tool Calling. A Playbook for Under-Represented Languages
We trained Agaram 2.5B, a 2.5-billion-parameter Tamil-English language model, from scratch on about 50 billion tokens (58% Tamil, the rest English, math, code and parallel text) on one node of eight…FedGraph: Defending Federated Large Language Model Fine-Tuning Against Backdoor Attacks via Graph-Based Aggregation
Federated fine-tuning of large language models (LLMs) allows multiple clients to collaboratively adapt models without sharing raw data, offering strong privacy benefits.How Well Do LLMs Perform in Peer Review? Evidence from Finance and Accounting
Referees increasingly use large language models (LLMs), while finance and accounting journals disagree about whether to allow it.STAMP as a topology-aware molecular language for fragment-level learning and editing
Molecular fragments provide chemically meaningful units, but existing molecular languages rarely make their locations and connections explicit for editing.The Singularity Has a Distribution: Exact Timing Laws and Detection Limits for a Stochastic Intelligence Explosion
Automating AI research creates a positive feedback loop between software capability and research effort.Thinker-WM: A Unified World Model with Shared Multimodal Representations for Embodied Intelligence
World models have emerged as a promising paradigm for embodied AI, yet existing systems often remain fragmented in the modalities they model and the functions they support.Autonomous Science Bench: Evaluating AI Agents for Autonomous Scientific Discovery
AI agents for autonomous scientific discovery must decide which experiments to run under a limited budget.Learning Perturbation-Specific Representations through Conditional Energy Prior for Cellular Response Prediction
AI-based cellular perturbation prediction is essential to accelerate drug discovery and therapeutic identification.Readout Scaling Shapes Sparse Feature Recovery
Readout scaling changes what a sparse autoencoder (SAE) learns, but the benefit of removing it depends on the data.MemoryVLN: From Ego-Aware Memory Representation to Cross-Context Memory Reuse in Vision-Language Navigation
Large vision-language models (LVLMs) have shown strong potential for embodied vision-language navigation (VLN), yet their performance degrades on long-horizon trajectories due to context-length…ReTerm: Recovery Training for Self-Improving Terminal Agents
Terminal agents have shown strong abilities in complex coding tasks.