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Artificial Intelligence
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The newest 5,000 papers, from 11 October 2026 to 5 November 2026: 4,998 arXiv preprints and 2 OpenAlex records.
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Newest papers, page 172
Evolving Self-tuning Benchmarks for Long-Horizon Tool Use
For AI assistants to move beyond simple chat interfaces and reliably assist users with applications like Gmail, Venmo, and Slack, we need to improve their tool-use capabilities.Steering Vision-Language Models for Visual-Contextual Privacy in Location Disclosure
Vision-language models (VLMs) can infer sensitive information such as geolocation from subtle visual cues, creating privacy risks when responses disclose more detail than the visual context warrants.Unsupervised Tool Evolution for Coding Agents in Data-Lake Analytics
Coding agents can answer analytical questions over a data lake by discovering, cleaning, and joining the relevant data.Modeling Authentic Research Trajectories: A Graph View of Research Ideation
The research idea in a published paper is rarely the project's initial idea.An LLM-Based Agent for Legal Compliance Assessment: Architecture, Memory, and Evaluation in a Canadian and Quebec Context
Checking whether a text complies with a body of law is slow when done by hand and fragile when done with keyword rules.Refuses When Asked, Complies When Acting: A Runtime Safety Check for Robot Control Loops
Frontier models can control robots through agent harnesses that loop on one question: what motion comes next toward the user's goal?Exposing Vulnerability Monoculture Through Systemic Adversarial Robustness
Beneath the apparent diversity of foundation models lies an increasingly homogeneous technical stack of common training pipelines, distillation methods, and data.LiBRA: Logit Fingerprints for Byzantine-Robust Client Selection in Federated Learning
Byzantine-robust aggregation shields federated learning from malicious updates, but the rules that filter out suspect updates do so by comparing clients' parameter vectors.On the Limits of Scale: Inverse Scaling in Vision-Language Models
Scaling laws suggest that increasing model size, training data, and compute generally yields monotonic performance improvements.Predictive Closure: A Set-Level Characterization of JEPA's Feature Selection
In joint-embedding predictive architectures (JEPA), an encoder and a predictor are trained together to produce a representation that minimizes the error in predicting the representation of the next…Trail-Weighted Influence Scores for Approximate Outcome Prediction in Bayesian Networks
We study trail-weighted pruning of evidence for Bayesian-network prediction.Fundamental Impossibility Results in AI Safety: Trading Off between Desiderata
Is there a fundamental tradeoff between desiderata for safe AI systems, such as harmlessness and helpfulness?REM: What to Store, When to Recall, What to Forget in Experience Memory for Coding Agents
Large language model (LLM) agents now autonomously complete long-horizon tasks, such as repository-level coding, through hundreds of think-act steps, yet the experience each run produces is rarely…Correcting Attribution Patching with a Few Exact Measurements
Mechanistic interpretability asks which language model components matter for a behavior, and activation patching answers by changing one component's activation and measuring its effect, at a cost…When PTQ Calibration Helps or Hurts: Dose-Driven Collapse and Validation-Gated Abstention in Quantized Text Embedders
Learned post-training quantization (PTQ) calibration fits quantizer parameters on a small calibration set, and for a text embedder the natural supervision is retrieval relevance.Unified Sharp Bounds on Probabilities of Causation
Probabilities of causation (PoCs) have developed steadily, from the binary measures first introduced by Pearl to the non-binary setting recently studied by Shu et al. As the theory has matured, PoCs…ProMemAgent: Long-Horizon Proactive Assistance with Context-Aware Prospective Memory from Sensory Perceptions
Large language model (LLM) agents are moving beyond reactive responses toward proactive assistance that anticipates user needs without explicit requests.More Memory, Worse Agents: Error Reproduction and Anti-Persistence in LLM Agents
LLM agents can improve across tasks without updating model parameters by storing experience from their own executions in external memory and reusing it to guide later decisions.VHDLBench: Benchmarking and Fine-Tuning Large Language Models for VHDL-Based Chip Design
The rapid evolution of digital circuit complexity demands innovative approaches to hardware design automation.VerifierBench: Benchmarking the Generation of Executable Verifiers for LLM Agents
Agents need reliable ways to validate their outputs, both when completing tasks and when filtering synthetic training data.