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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 243
AMemOps: Evaluating Memory Updates in Long Audio Streams
Audio memory must recognise recurring sources across acoustic changes while keeping their attributes up to date.Kwai Summary Attention V2 Technical Report
token architectures such as Kwai Summary Attention (KSA) compress distant context into learnable Summary token per chunk, and hybrid variants interleave these layers with full attention to retain…Known-Voice Cloning and the Limits of Personality Transfer for Character Design
Recent advances in zero-shot voice cloning systems make it possible to reproduce a recognizable voice from a short recording and attach it to arbitrary virtual characters.SMI: Ontology-Grounded Semantic IDs for Hierarchy-Aware Medical Concept Representation
Biomedical language models (LMs) provide strong representations for clinical tasks, but they often fail to capture the hierarchical structure among medical concepts.From Relation-Specific Heads to Shared Interventions: Contextual Entrainment in Language Models
Large language models rely on prompt context to perform in-context learning, follow formats, and adapt to local evidence.Model-Based Online Decision Making via Generative Trajectory Planning
Online decision-making faces a tension between efficient inference and long-horizon reasoning.ProAS: A Progress-Aware Advantage Shaping Framework for Multi-Turn Agentic Reinforcement Learning
Reinforcement learning (RL) has emerged as the primary paradigm for optimizing Large Language Model (LLM) agents, yet efficient critic-free algorithms (e.g., GRPO) suffer from a fundamental uniform…Knowing the User Is Not Knowing What We Established: Grounding Awareness in Personal Agents
Personal-agent memory has largely focused on learning what users know, prefer, and want.RadarGSOcc: Doppler-Aware 3D Semantic Occupancy Prediction Using 4D Radar Tensor with Geometric-Semantic Feature Extraction
3D semantic occupancy prediction is crucial for autonomous driving, but camera-based methods degrade in low-light conditions and LiDAR-based methods are sensitive to adverse weather.Just Respect the Semantics: Image-Token Compression via Pre-LLM Semantic Variable-Resolution Tokenization and In-LLM Relevance Pruning
Image-token compression must reduce computation without hurting the semantics.GeoReader: Learning Latent Geometry for Spatial Reasoning in Vision-Language Models
Vision-language models (VLMs) struggle with metric and multi-view relationships that remain implicit in RGB observations.UniW(A)M: A Unified Mixture-of-Experts Framework for World and World-Action Models
World models (WMs) and world-action models (WAMs) focus on future world prediction and executable action generation, respectively.Unlocking Token Rewards via Training-Free Reward Attribution
In this paper, we propose an extremely efficient, training-free method to extract token-level reward signals directly from an existing deep reward model.Steertuning Policies from a Few Human Videos
Current recipes for robot learning require massive amounts of robot demonstrations to obtain robust and adaptive policies.A Rate Transfer Method: Convergence Rates in Stochastic Gradient Descent via Stochastic Calculus
We develop a rate-transfer method that converts finite-time Lyapunov estimates for an associated stochastic differential equation (SDE) into finite-time convergence bounds for stochastic gradient…SolBehave: Evaluating the Behavioural Correctness of LLM-Generated Smart Contracts
Large language models increasingly generate Solidity smart contracts that, once deployed, can never be patched, making a single latent fault permanent and exploitable.PreciseCoder: Faithful and Generalizable Debugging Beyond Unit-Test Rewards
Reinforcement learning (RL) from unit tests is the common recipe for post-training code models, but a unit test only asks whether the final program passes.Beyond the Verdict: Agentic Code Judging with Verifiable Feedback
Test-time scaling for large language models (LLMs) can involve generating multiple candidate solutions for selection or revising an earlier attempt.Jeva: System One Models from First Principles
System one models like Jev enable direct, low-latency decision-making.BLADE: Auditing Information Use for Failure Attribution in LLM Multi-Agent Systems
Large language model multi-agent systems extend individual agents' capabilities through collaboration across specialized roles, tools, and workflows.