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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 205
GRADS: Graph-Regulated Agentic Data Synthesis with Controllable Difficulty
Deep research agents are increasingly deployed to retrieve precise information from large corpora, yet multi-hop, long-horizon tasks remain challenging.MemScope: Evolving Agent Memory Architectures through Localized Code Repair
Agent memory architectures define how LLM agents store and reuse experience across tasks, but they are typically fixed after deployment, limiting their adaptability across task types.Language Models Share a Readout Bottleneck in Working Memory
Large language models (LLMs) reproduce a human working-memory signature on the N-back task: performance declines as memory load N increases, and errors concentrate on lures, items that repeat the…AgentEval: Evidence-Bounded Assessment of Stateful Agents
Task completion alone does not determine whether a stateful agent satisfied its obligations, and a recorded judgment does not guarantee that its supporting evidence remains valid.Judging the Judges: Confidence and Disagreement in Rubrics as Rewards
Rubrics as Rewards (RaR) extends reinforcement learning to non-verifiable domains by using an LLM judge to score responses against a structured rubric.Does Latent Chain-of-Thought Actually Reason? A Single, Unifying Taxonomy for Latent Reasoning
Latent chain-of-thought replaces written reasoning steps with continuous hidden states, giving models additional steps to compute before answering.Mine, Sample, Render: Test-Time Diversity Alignment Of Generative Models
Modern generative models produce high-quality individual outputs but struggle to sample diversely, even when explicitly instructed to do so.FedPix: Learning What to Share with Less Leakage in Federated Intelligence
Existing federated intelligence paradigms primarily focus on how models are trained or aggregated, while overlooking a more fundamental question: \textbf{what information should leave the client?}…Condition-Contrastive Extrapolation for On-Policy Distillation of Language Models
On-policy distillation (OPD) improves a student language model by training on its own generated trajectories with supervision from a stronger teacher.Who May Release an Agent's Promise? Standing over Commitments in Multi-Party Chat
When an assistant serving a team makes a promise to Alice, who may cancel it?Entertain, then Cancel: How Thinking Makes Reasoning Models Read Some and Or Literally
When a speaker says ``some students passed'', listeners infer that not all of them did.Rubric Judgments Lack Uncertainty: Conformal Weighting for LLM Evaluation
Rubric-based evaluation is widely used to assess large language model (LLM) outputs by decomposing quality into multiple criteria and assigning each a binary judgment from an LLM verifier.AutoOnboarder: Autonomously Onboarding LLM Agents
Agentic large language models have been documented to lose reasoning capability when placed inside a harness, the software that mediates their observations and actions.RecSoup: Efficient Model Merging Approach for Controllable Multi-Objective Recommendation
Recommender systems often need to balance multiple conflicting objectives.Representation Redistribution in Sparse Autoencoders
Sparse autoencoders (SAEs) decompose language model activations into sparse features, but prompt transformations designed to preserve the underlying request can redistribute activity across them.A Responsibility-Centered Coordination Mechanism for Decentralized Multi-Agent Systems
Centralized orchestration provides comprehensive multi-agent coordination but creates bottlenecks in scalability, adaptability, and collective performance.System1-Arena: Which Decisions Can Be Left to System 1?
Large language models (LLMs) often make discrete decisions, such as flagging phishing or escalating to a human.What do AI chatbots do after users decline therapeutic redirection for suicide thoughts?
Safety evaluations of artificial intelligence (AI) chatbots focus on therapeutic redirection, statements urging users who disclose suicide thoughts to seek help from crisis lines or other human…Fractional Power-of-Two Quantization for Efficient and Effective Multiplier-Free LLM Inference
Large Language Models (LLMs) demand efficient inference, where the multiply-accumulate (MAC) operations in linear projections dominate compute and energy cost.Behavioral Compositionality of Sparse Autoencoder Representations
Sparse autoencoders (SAEs) represent model activations with sparse, linearly decoded features, but the downstream effects of those features need not compose additively.