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Qwenfant: Train Big Character-level BabyLMs on Concise Parallel Text

arXiv:2610.19294v1 Announce Type: new Abstract: This work concerns our submission for the Multilingual track of the 2026 BabyLM Challenge. We improve on our baseline using data that has been summarize

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RA^2: Retain-Anchored Attraction Preserves Forget-Adjacent Utility in LLM Unlearning

DGX agent

arXiv:2610.19385v1 Announce Type: new Abstract: Large Language Model (LLM) unlearning is often evaluated primarily by target suppression and clean retain utility. These metrics can miss a local failur

local-aiarxiv-cs-lg

15 Oct 2026

Radioactive Feedback: Tracing Models Trained by Label-Only LLM Supervision

arXiv:2610.15685v1 Announce Type: new Abstract: Conventional distillation transfers capability through teacher-generated demonstrations. LLM-as-a-Judge knowledge distillation offers a distinct and inc

RadixFlow: Filling Execution Bubbles in Prefix-Cached Hybrid LLM Serving

arXiv:2610.18143v1 Announce Type: new Abstract: Prefix reuse improves LLM inference by sharing the KV cache of common prompt prefixes. This helps only if the scheduler can execute a matched branch in

RAN-JEPA: An Action-Conditioned World Model for Energy-efficient Mobile Network Control

arXiv:2610.17584v1 Announce Type: new Abstract: Operational mobile networks are rich in observations but poor in interventions: they continuously generate large volumes of passive telemetry, while con

Randomly Wrong Signals: Bayesian Auction Design with ML Predictions

arXiv:2502.08792v4 Announce Type: replace-cross Abstract: Machine learning can help sellers predict buyers' willingness to pay, but its predictions may be plausible and yet entirely unrelated to a buy

RAP-WAM: Learning World-Action Models through Predictive Rehearsal and Action Perturbations

arXiv:2610.16443v1 Announce Type: new Abstract: Recent world action models draw on video generation priors to jointly predict future observations and robot actions. Adapting these priors to control re

safetyarxiv-cs-ai

RASSG: Repair-Aware Semantic Survival Gating for Streaming Tool Execution

arXiv:2610.14659v1 Announce Type: new Abstract: Streaming speech assistants can launch tools before users finish speaking, overlapping execution with incoming speech to reduce user waiting time. Howev

Rational Transductors

arXiv:2602.07599v2 Announce Type: replace Abstract: Standard Transformers excel at semantic modeling but struggle with rigid sequential logic and state tracking. Theoretical work establishes that self

researcharxiv-cs-lg

Rationale Distribution Distillation via Flow Matching for Distributional Continuous Latent Reasoning

arXiv:2610.19871v1 Announce Type: new Abstract: Sampling multiple chain-of-thought rationales from a language model can uncover correct solutions missed by a single attempt. However, these rationales

RCCM: Diagnosing Gains and Harms in Reasoning-Prompt Transfer

model-releasesarxiv-cs-cl

arXiv:2610.19872v1 Announce Type: new Abstract: A reasoning prompt can improve one deployment and degrade another. RCCM (Reasoning-Control Capability Mismatch) examines these reversals by pairing answ

Agents

RCR-Router: Efficient Role-Aware Context Routing for Multi-Agent LLM Systems with Structured Memory

arXiv:2508.04903v4 Announce Type: replace-cross Abstract: Large language model (LLM) agents are increasingly organized into multi-agent systems for complex reasoning and decision-making tasks. However

agentsarxiv-cs-ai

RDCompGS: Rate-Distortion Optimization for Compression of 3D Gaussian Splatting with Image Codecs

model-releasesarxiv-cs-cv

arXiv:2610.19538v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) enables high-quality, real-time novel view synthesis but requires significant storage and transmission resources. Mapping G

REA: From Correct LLM Judgments to Safe Decision Delivery

arXiv:2610.17473v1 Announce Type: new Abstract: Generative safety guards must classify requests correctly and return outputs that applications can parse. Reasoning guards can improve classification ac

safetyarxiv-cs-ai

Read, Parse, Describe: Unified Document Parsing with Visual Element Description Generation

model-releasesarxiv-cs-cv

arXiv:2610.20335v1 Announce Type: new Abstract: Document understanding systems are typically evaluated as separate components via layout analysis, optical text recognition, reading-order prediction, a

Read the Room Before Your Model Generates

arXiv:2610.18820v2 Announce Type: new Abstract: Large language models (LLMs) generally rely on sampling temperature to regulate the amount of randomness in their outputs. Mainstream methods for select

Reading Right, Acting Wrong: Diagnosing Decision Failures of System One Models in Agent Loops

arXiv:2610.18579v1 Announce Type: new Abstract: System One models choose an agent's next action by scoring a supplied set of options in a single pass. This makes them fast and cheap, but when they cho

ReaDy: Recurrent States as Dynamic Context for Speculative Drafting in Hybrid Linear Attention Large Language Models

model-releasesarxiv-cs-cl

arXiv:2610.20503v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive generation by drafting multiple tokens per step, yet feature-based drafters rely on target hidden featur

ReAl: Refine neural operators via aligning dynamical flow

arXiv:2610.20369v1 Announce Type: new Abstract: Neural partial differential equation (PDE) surrogates trained with mean squared error (MSE) can capture dominant dynamics but often lose high-frequency

Reasoning for Propaganda Detection via Label-Guided CoT Optimisation

arXiv:2610.17271v1 Announce Type: new Abstract: Propaganda detection systems usually operate as black boxes, lacking explanations. To address this, we hypothesise that persuasive techniques contain de

Agents

Recall What Is Missing: Composing Evidence from Long-Term Agent Memory

arXiv:2610.15642v1 Announce Type: new Abstract: Under a limited context budget, a long-term agent must recall not merely memories that are individually relevant, but evidence that is collectively suff

agentsarxiv-cs-ai

RECAP: Calibrating Low-Bit Reasoning Models on the History They Read

arXiv:2610.15259v1 Announce Type: new Abstract: Low-bit quantization reduces the cost of serving reasoning models but can substantially degrade long chains of thought. Each step reads cached keys and

Receding-Horizon Next-Best-View Planning for Highly Occluded Target Perception

arXiv:2610.19082v1 Announce Type: new Abstract: Robotic systems that must acquire information about a specific target often face severe occlusion from sur- rounding structures or from the target itsel

researcharxiv-cs-ro

Reclaiming Low-Quality Instruction Data with Rule-Guided LLM Reconstruction

arXiv:2510.23160v2 Announce Type: replace Abstract: Data selection for supervised fine-tuning (SFT) often discards low-scoring instruction examples that may retain useful supervision. We propose a rul

Recognizing Dialogue Ambiguity via Candidate Set Visual Grounding

model-releasesarxiv-cs-cl

arXiv:2610.19723v1 Announce Type: new Abstract: Interpreting an ambiguous utterance in real-world interactions often requires context beyond a single utterance. When the utterance refers to objects in

Recovering Reliable Supervision via Critique-Guided On-Policy Distillation

arXiv:2610.20572v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student model on its own rollouts with token-level supervision from a teacher, assuming that the teacher can reach

Agents

ReCreate: Reasoning and Creating Domain Agents Driven by Experience

arXiv:2601.11100v3 Announce Type: replace Abstract: Large Language Model (LLM) agents are reshaping the industrial landscape. However, most practical agents still rely on human-designed scaffolds, mak

agentsarxiv-cs-ai

Recursive Environment Scaling for General Long-Horizon Agents

arXiv:2610.16397v1 Announce Type: new Abstract: We study environment scaling as a way to increase agent-benchmark difficulty while holding core task objectives fixed. Our framework, Recursive Environm

Recursive Improvement of a Differentiable Scientific Software Ecosystem

arXiv:2610.04561v2 Announce Type: replace Abstract: Differentiable programming connects scientific computation with gradient-based inference, learning and design. Extending these capabilities across a

Reducing the Gap Between Spatial and Space-Filling Curve Neighbors in 3D Point Cloud Segmentation

arXiv:2610.19530v1 Announce Type: new Abstract: As a lightweight and highly accurate method for nearest-neighbor search in 3D point clouds, approaches based on space-filling curves (SFC) have been pro

researcharxiv-cs-cv

Reels-RSI: Program-Level Self-Improvement for Code-Rendered Explainer Videos

arXiv:2610.14495v1 Announce Type: new Abstract: Explainer videos -- a worked geometry problem, why the sky is blue, how a dynasty fell -- are usually one more target for pixel video generation. We arg

Tutorials

ReferFlow: Beyond Final-Target Localization for Complex Referring Expressions

arXiv:2610.20352v1 Announce Type: new Abstract: Multimodal Large Language Models have made significant progress in referring expression comprehension (REC), yet most REC benchmarks still focus on fina

tutorialsarxiv-cs-cv

Refine Actions If Needed: Adaptive Action Chunk Refinement for Robot Manipulation

arXiv:2610.19765v1 Announce Type: new Abstract: Computationally lightweight manipulation policies predict executable actions with low inference latency, but prediction errors can limit task success. I

safetyarxiv-cs-ro

Refined Topology-Pass Scheduling: Damped Trust-Region Compensation for Dynamic k-NN Graphs in SE(3)-Equivariant Structure Prediction

arXiv:2610.19246v1 Announce Type: new Abstract: Equivariant structure-prediction pipelines alternate coordinate updates with the construction of a k-nearest-neighbour (kNN) graph whose membership is a

researcharxiv-cs-lg

ReGRA: Residual-Gradient Adaptation for Complementary Low-Rank Fine-Tuning

model-releasesarxiv-cs-lg

arXiv:2610.19700v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) enables parameter-efficient fine-tuning by learning low-rank weight updates. Increasing rank expands adaptation capacity, but

Reinforcement Learning via Advantage Guidance

arXiv:2610.19783v1 Announce Type: new Abstract: Reinforcement learning (RL) holds the promise of learning a policy that can outperform what is shown in the data. However, in practice, such systems suf

Relational Semantic Grounding for Test-Time Discovery

arXiv:2610.14721v1 Announce Type: new Abstract: Test-Time Discovery (TTD) aims to recognize known classes and detect novel ones from a continuous unlabeled test stream after deployment. Existing TTD m

safetyarxiv-cs-ai

Releasing the Inner Jev: A Simple Recipe for Open Decision Models

arXiv:2610.17205v1 Announce Type: new Abstract: LLM-based decision-making systems, also called System One models, have recently gained popularity, most notably through Jev (Almeida, 2026). Given a typ

Relevant to the comprehension challenge I posed in 2014 and my bet with Miles Brundage.

model-releasesgary-marcus--x

Relevant to the comprehension challenge I posed in 2014 and my bet with Miles Brundage. We edited Mark Twain's Huckleberry Finn and fed it to 9 AI models to summarize a chapter, twice each. Only Claud

Local Ai

Reliability-Aware Multimodal Sentiment Prediction Under Local Modality Missingness

arXiv:2610.20543v1 Announce Type: new Abstract: Multimodal sentiment models are usually evaluated on complete utterances, yet streams often lose a contiguous span of text, audio, or video. We study lo

local-aiarxiv-cs-cv

Reliable Text-to-SQL in Resource-Constrained Settings with Small LLMs via Iterative Retry Workflows

arXiv:2610.17305v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently improved Text-to-SQL generation, but their computational cost can hinder deployment in resource-constrained s

Local Ai

Remember, Revisit, Revise: Scaffold Memory for Long-Horizon 3D Geometry

arXiv:2610.19674v1 Announce Type: new Abstract: Geometry foundation models estimate camera poses and 3D scene structure from video sequences, but maintaining consistent reconstructions over long horiz

local-aiarxiv-cs-cv

Rent-Seeking Language Models: Reward Models Set the Prices, Frequent Audits Curb the Rent

arXiv:2610.19832v1 Announce Type: new Abstract: Reward model overoptimization in RLHF is usually described as reward hacking. We recast it as rent-seeking: the policy, a firm, earns reward from a regu

Repairing Speech Access After Text Updates with Frozen Affine Fields

arXiv:2610.20253v1 Announce Type: new Abstract: Speech-capable language models inherit most of their knowledge from text-rich language backbones, and continued model maintenance is likewise dominated

RepForcing: Representation-Space Initialization for Autoregressive Video Distillation

arXiv:2610.20111v1 Announce Type: new Abstract: Distilling pretrained video diffusion models into few-step autoregressive (AR) generators typically combines student initialization with distribution ma

researcharxiv-cs-cv

Representation Conditioning for Generation

arXiv:2610.20608v1 Announce Type: new Abstract: Representation-alignment methods effectively accelerate the diffusion transformer training. REPA, for example, uses a pretrained vision encoder to super

safetyarxiv-cs-cv

Representational Similarity Alignment of Figurative Language in Humans and Large Language Models

arXiv:2601.09041v2 Announce Type: replace-cross Abstract: Large language models (LLMs) often produce judgments similar to those of humans, but their interpretation of figurative language remains uncle

REPRO: Reflective Evolutionary Prompt Optimizer for Automated LLM Red-Teaming

arXiv:2610.15513v1 Announce Type: cross Abstract: Safety-aligned large language models (LLMs) remain vulnerable to adversarial prompts that bypass their safety training. Existing automated red-teaming