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Learning to Simulate Individuals from Macro Social Signals

arXiv:2610.07062v1 Announce Type: cross Abstract: Large language models are increasingly used to simulate how individuals respond to new situations, yet the behavioral reasoning behind these responses

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Learning What to Distill: Bilevel Top-K Token Selection for Self-Distillation in Large Language Models

DGX agent

arXiv:2610.07247v1 Announce Type: new Abstract: Large language models have shown strong reasoning capabilities, but their high inference costs make knowledge distillation an important approach for tra

safetyarxiv-cs-lg

7 Oct 2026

Lineage-Aware Memory Governance: A Derivation-Gated Framework for Privacy-Preserving Column-Level Access Control in Enterprise AI Agents

arXiv:2610.07258v1 Announce Type: cross Abstract: Enterprise AI agents that share a memory store face two unaddressed risks: sensitive data can leak through legitimately computed results the requester

Linear Bandits under Exact Sliding-Window Constraints

arXiv:2610.08745v1 Announce Type: new Abstract: We study linear bandits under exact sliding-window constraints, where every consecutive block of actions must belong to a prescribed feasible set. In th

Making COMET Comparable Across Scripts: Diagnosis and Correction of Tokeniser-Induced Script Bias in Indic MT Evaluation

arXiv:2610.08159v1 Announce Type: new Abstract: COMET reports translation quality as a single number, and that number is routinely compared across target languages written in different scripts. Such a

Micro Neural Policies for Safe Real-Time Robotic Control

arXiv:2610.08541v1 Announce Type: cross Abstract: In this paper, we investigate the synthesis of Micro Neural Policies (MNP) to enable safe and robust real-time robotic control on computationally cons

Minimal Witness Reinforcement Learning

arXiv:2610.07226v1 Announce Type: cross Abstract: ``What are the irreducible conditions that are sufficient to produce an outcome?'' is one of the most common questions that recur across computation a

Modeling Latent Disturbances for Robust Decision-Making in World Models

arXiv:2610.07599v1 Announce Type: cross Abstract: In this paper, we study robust decision-making in the latent space of world models (WMs). Robust optimization is a mathematical framework where, given

MS-ECG-FM: Towards a More Universal Electrocardiogram Foundation Model for Health Monitoring using Multi-source Contrastive Learning

arXiv:2610.07662v1 Announce Type: new Abstract: Electrocardiography (ECG) records the electrical activity of the heart, aiding diagnosis by detecting abnormalities in cardiac function. ECG foundation

Multi-Robot Multi-Goal Motion Planning with Stochastic Skills

arXiv:2610.07784v1 Announce Type: new Abstract: As robots are increasingly deployed in groups and share workspaces to execute real-world tasks, planning their concurrent motions around complex manipul

Nash Social Welfare for Multi Armed Bandits: Trajectory-wise Expected and High Probability Regret

arXiv:2610.07737v1 Announce Type: cross Abstract: We study fair multi-armed bandits under the Nash Social Welfare (NSW) objective, which measures performance via the geometric mean of accumulated rewa

Neural Algorithmic Reasoning for Graph Saddle Point Problems

arXiv:2610.07255v1 Announce Type: new Abstract: Neural algorithmic reasoning, or aligning a neural network with an algorithmic paradigm, has emerged as an approach to solving polynomial-time-solvable

Not What a Child Expressed: Auditing the Sign-to-Text Safety Interface in Child-Facing AI

arXiv:2610.07519v1 Announce Type: new Abstract: Automatic sign language translation (SLT) has entered consumer products, turning American Sign Language into English text for dictation, messaging, and

On Color Alignment in VAE Latent Spaces and Its Applications

arXiv:2610.07072v1 Announce Type: new Abstract: Variational autoencoders (VAEs) are a key part of modern text-to-image models, which generate images within their latent space. VAEs are known to disent

On-Policy Distillation with Negative-Policy Rollouts

arXiv:2610.07874v1 Announce Type: new Abstract: On-policy distillation (OPD) has been widely studied as a post-training method in which a student model obtains token-level supervision from a stronger

On the Computational Tractability of Robust Bandits

arXiv:2610.08740v1 Announce Type: new Abstract: Learning when the environment does not belong to the learner's hypothesis class is typically handled using agnostic learning guarantees. However, for an

Optimal Transportation and Alignment Between Gaussian Measures

arXiv:2512.03579v2 Announce Type: replace Abstract: Optimal transport (OT) and Gromov-Wasserstein (GW) alignment provide interpretable geometric frameworks for comparing, transforming, and aggregating

Optimization Encoders: Rethinking Second-Order Meta-Learning for Neural Fields

arXiv:2610.08075v1 Announce Type: cross Abstract: Conditional neural fields represent signals continuously, but their effectiveness depends on how the conditional latent representations are inferred f

PACE: Stage-Consistent Long-Horizon Robot Manipulation via Progress-Aligned Context for Execution

arXiv:2610.07917v1 Announce Type: new Abstract: Demonstration-conditioned policies provide a natural interface for specifying robot behavior, yet long-horizon manipulation remains difficult when visua

PEARS: Physical-Prior-Guided Efficient Adaptation via Failure Reasoning and Diffusion Steering for Tactile Manipulation

arXiv:2610.08784v1 Announce Type: new Abstract: Pretrained robotic policies can suffer substantial performance degradation under out-of-distribution (OOD) conditions encountered during deployment, mot

Performative Prediction with Selective Labels

arXiv:2610.08272v1 Announce Type: new Abstract: Many social applications of machine learning exhibit performative effects: population behavior changes in response to deployed models. Performative pred

Physical Twins: Accelerating and Enabling Robot Learning with Phantom Platforms

arXiv:2610.06929v1 Announce Type: new Abstract: Improvements in human-robot physical interaction (pHRI) can have major implications for physical therapy, search and rescue, and telemedicine. However,

PhysTacGen: Physics-Aware Visual-Tactile Sensor Image Generation

arXiv:2610.08068v1 Announce Type: new Abstract: Realistic physical interaction is a cornerstone of embodied intelligence, yet collecting paired visual--tactile data remains costly. Visual-to-tactile s

POLAR: Ontology-Guided Risk Prevention for Tool-Calling LLM Agents

arXiv:2610.08082v1 Announce Type: new Abstract: LLM tool-use agents operate in dynamic environments where many actions carry operational risk. However, most safety mechanisms react only after errors m

Prediction-powered inference for time series across space

arXiv:2610.08715v1 Announce Type: cross Abstract: The following motif is common in spatiotemporal settings: we have a sequence of covariate and label pairs observed for a relatively short, recent time

Pronunciation Editing for Finnish Speech using Phonetic Posteriorgrams

arXiv:2507.02115v3 Announce Type: cross Abstract: Synthesizing second-language (L2) speech is potentially highly valued for L2 language learning experience and feedback. However, due to the lack of L2

Pseudowords as probes: Large Language Models show little of the sublexical sensitivity that governs human pseudoword processing

arXiv:2610.07936v1 Announce Type: new Abstract: Systematicity, the probabilistic mapping of form to meaning, permeates language at all levels, and sublexical cues have been shown to govern human pseud

PsyCIDRA: A Dual-Agent Framework for Psychiatric Interviewing and Diagnostic Reasoning

arXiv:2610.07473v1 Announce Type: new Abstract: Large language models show promise in clinical reasoning, but psychiatric interviewing requires guiding an evolving conversation. Their ability to carry

QF3: Fast Flow RL with Filtered Q-Gradients

arXiv:2610.08789v1 Announce Type: cross Abstract: Flow policies have become a standard policy class for learning robot behaviors from demonstrations, but reinforcement learning is still critical for i

RACER: Residual-Adaptive Closed-Loop Estimation for Sampling-Based Planning in Wheeled-Quadruped Racing

arXiv:2610.07409v1 Announce Type: new Abstract: We present RACER, a hierarchical control framework for wheel-based quadruped racing that combines an MPPI planner with a learned residual dynamics model

RADC: Risk-Aware Dual Caching for Vision-Language Test-Time Adaptation

arXiv:2610.06932v1 Announce Type: new Abstract: Cache-based test-time adaptation (TTA) for vision-language models is often hindered by background bias in global representations and unreliable entropy-

Random Feature Gaussian Process Attention: Linear-Time Probabilistic Attention with Calibrated Uncertainty

arXiv:2610.08578v1 Announce Type: new Abstract: Transformers provide a state-of-the-art modeling framework, yet poor calibration limits their reliability in safety-critical applications. A promising d

Rationale-Guided Policy Optimization: Learning to Reason with Adaptive Rationale Scaffolding

arXiv:2610.07342v1 Announce Type: new Abstract: On-policy reinforcement learning has become a central paradigm for improving the reasoning abilities of large language models. However, its effectivenes

RBMatch: Dual-Level Class Rebalancing for Semi-Supervised Building Footprint Extraction

arXiv:2610.07698v1 Announce Type: new Abstract: Accurate building footprint extraction from high-resolution remote sensing imagery is essential for urban planning, disaster response, and environmental

Recursive Game Creator: An Agentic Product-Level Experience-Oriented Game Harness

arXiv:2610.08621v1 Announce Type: new Abstract: Recent game design agents have made substantial progress in generating playable games. However, program correctness does not ensure an enjoyable experie

ReDex: Repairing Sim-to-Real Dexterous Policies by Finger-Level Compliant Interaction

arXiv:2610.07525v1 Announce Type: new Abstract: Dexterous manipulation policies trained in simulation often fail to transfer to the real world because of errors in contact timing and force regulation.

Reinforcement Learning for Hierarchical Reasoning Rewards: Minimax-Optimal Rates with Transformers

arXiv:2610.08561v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a standard tool for post-training language models on reasoning tasks, where the policy is updated by reward feedb

REPA-G: Test-Time Conditioning with Representation-Aligned Visual Features

arXiv:2602.03753v2 Announce Type: replace Abstract: While representation alignment with self-supervised models has been shown to improve diffusion model training, its potential for enhancing inference

Representation Bias, Correction Transfer, and Resolution Sensitivity in Three-Dimensional Mitochondrial Morphometry

arXiv:2610.07582v1 Announce Type: new Abstract: Quantitative imaging pipelines can produce precise but systematically different measurements of the same object. We present an empirical reliability ass

Responsible Institutional Analytics: Interpreting Bias with AI Support

arXiv:2610.07205v1 Announce Type: cross Abstract: Institutional Analytics (IA) dashboards inform decision-making in higher education, yet data limitations, constraints in analytical techniques, and mi

Rethinking Cross-Tokenizer On-Policy Distillation: From Alignment Coverage to Supervision Reliability

arXiv:2610.08448v1 Announce Type: cross Abstract: On-Policy Distillation (OPD) trains a student on its own generations using teacher feedback. With different tokenizers, comparing teacher and student

Retrieval Is Not Enough: Refreshing Memory for Frozen Time-Series Forecasters

arXiv:2610.07834v1 Announce Type: new Abstract: Retrieval-augmented time-series forecasting uses the continuations of historical segments similar to the current context as references for a forecaster.

Revar3r: gauge-aware perturbation uncertainty for feed-forward 3d reconstruction

arXiv:2610.07883v1 Announce Type: new Abstract: A correctly reconstructed distant point appears uncertain even when a frozen 3D model processes equivalent inputs because its output frame rotates fract

Revisiting Numerical Forecasting Models for Language-Based Trajectory Prediction

arXiv:2610.07954v1 Announce Type: new Abstract: Language-based trajectory predictors represent coordinates as discrete tokens and learn auxiliary tasks such as destination and group reasoning. This fo

Revisiting Temporal Regularization for Smooth Control in Deep Reinforcement Learning

arXiv:2610.07910v1 Announce Type: new Abstract: Deep Reinforcement Learning policies can produce nonsmooth action oscillations that hinder deployment on physical robots. Existing architectural and pen

Risk-Sensitive Crowd Navigation with Adaptive Ellipsoidal Conformal Prediction

arXiv:2610.07474v1 Announce Type: new Abstract: Safe crowd navigation under distribution shift requires uncertainty representations that capture structured human-motion prediction errors and safety ob

Safeguarding LLMs via Model-Agnostic Latent Safety Signals from Dark Knowledge

arXiv:2610.07532v1 Announce Type: cross Abstract: LLMs have advanced rapidly, raising growing concerns about their safety. Recent work has proposed approaches to detect and defend against attacks incl

SAFESHIELD: A Decision-Organization Framework for Deployment-Time Safety of Small Language Models

arXiv:2610.07276v1 Announce Type: cross Abstract: Deployment-time safety of language models is commonly implemented through runtime guardrails such as input moderation, routing, retrieval verification