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ReproBAIT: Can coding agents build their own biological AI tools?

arXiv:2610.19810v1 Announce Type: cross Abstract: Large language models are increasingly competent at leveraging scientific knowledge and existing computational tools to execute complex workflows. At

agentsarxiv-cs-lg

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DGX agent

arXiv:2610.18204v1 Announce Type: cross Abstract: Connection-tableau provers typically construct closed tableaux by backtracking search. This gives lightweight proof procedures, but their low-memory s

model-releasesarxiv-cs-ai

15 Oct 2026

Resolution Allocation with Learnable Resizing and Spectral Filter Parameterization

arXiv:2610.20274v1 Announce Type: cross Abstract: Learnable downsampling and spectral-domain filter parameterizations have each made convolutional networks more flexible, but their interaction has not

ReTAC: Residual Test-Time Agentic Computation for Video Spatial Reasoning

arXiv:2610.16315v1 Announce Type: new Abstract: Test-time agentic computation can improve multimodal large language models (MLLMs) through planning, tool use, iterative perception, and reflection, but

agentsarxiv-cs-ai

RetailAgent: Structured Adverse Timing in Self-Conditioned Multimodal LLM Trading Agents

arXiv:2608.28399v2 Announce Type: replace Abstract: In financial markets, a sequential policy that reacts systematically to price movements may become predictable to other market participants. This pa

Retell, Reward, Repeat: Reinforcement Learning for Narrative Theory-Informed Story Retelling

arXiv:2601.17226v3 Announce Type: replace-cross Abstract: Counterfactual storytelling exposes LLM shortcomings in causal reasoning, where few-shot prompting and SFT fail to produce logical and rationa

Rethinking Prediction Horizons: Decoupling Model Queries from State Updates in Few-Step MeanFlow

arXiv:2610.20071v1 Announce Type: new Abstract: MeanFlow predicts interval-average velocities, but conventional few-step sampling ties the interval queried from the model to the duration of the state

Rethinking Quality in Large Language Model Routing: Security-Conditioned Model-and-Budget Selection

arXiv:2610.18566v1 Announce Type: new Abstract: Large language model (LLM) routers reduce serving cost by choosing which model to call and how many output tokens to grant it for each query. Existing r

Retrieval-Augmented Large Language Model Decision-Making for Autonomous Driving Guided by Chinese Philosophical Wisdom

arXiv:2610.03948v3 Announce Type: replace Abstract: Autonomous driving decision systems must balance safety, efficiency, and social norms in complex traffic interactions. Philosophical and ethical con

Retrieval Over Training: Similarity search-based Model Selection for Time Series Anomaly Detection

model-releasesarxiv-cs-lg

arXiv:2610.20243v1 Announce Type: new Abstract: Anomaly detection is a fundamental task for time-series analytics, with important implications for the downstream performance of many applications. Desp

Retrieve What Helps, Only When It Helps: Agentic Reinforcement Learning for Selective Retrieval-Augmented Machine Translation

arXiv:2610.19528v1 Announce Type: new Abstract: Retrieving external knowledge can improve large language model (LLM) translation by providing supplementary information such as idiom interpretations an

agentsarxiv-cs-cl

RetryBench: Separating Retry Permission from Recovery Outcomes in GPT Tool Use

arXiv:2610.15884v1 Announce Type: new Abstract: False retry permission (FRP) occurs when an agent retries a timed-out tool call without support from the visible contract. RetryBench separates this err

REVEAL: Reverse Engineering Agentic Latents from Frontier-Model Traces

arXiv:2610.16288v2 Announce Type: new Abstract: Frontier LLM agents perform complex interactive tasks, but their inference costs motivate transferring their experience to smaller open-source agents. D

Review Rarely Beats the Stronger Model: Separating Verification from Capability Transfer in LLM Pipelines

arXiv:2610.15897v2 Announce Type: cross Abstract: Qwen3-8B reviewing Llama-3.1-8B in math adds 33.2 points, yet Qwen3-8B alone is 6.4 points more accurate than the result. Reviewing another model's an

Revisiting Generative vs. Dense Retrieval: A Static Value Prior Reverses the Gap

arXiv:2610.19065v1 Announce Type: cross Abstract: Generative retrieval (GR) has emerged as a strong candidate generator for sequential recommendation as language models and semantic-item identifiers h

Revisiting Simple Baselines for Relational Deep Learning

arXiv:2610.20534v1 Announce Type: new Abstract: Relational deep learning has emerged as a powerful paradigm for learning end-to-end predictive models over relational databases by representing them as

Revocation Is Not Complete at Receipt: An Executable Outcome Contract for Distributed Agent-Tool Gateways

arXiv:2610.15991v1 Announce Type: new Abstract: Distributed agent workflows cache delegated authority at tool gateways. Revocation may be reported at issuer commit, transport acknowledgment, or receiv

agentsarxiv-cs-ai

REWIND: Uncertainty-Guided Trajectory Repair for Test-Time Reasoning

arXiv:2610.19561v1 Announce Type: new Abstract: An early mistake in the generated reasoning can constrain all subsequent reasoning. Repeated-generation methods, like MCMC-based approaches, attempt to

researcharxiv-cs-cl

Riding the Fixed-Point Curve: Image Classification in the Continuous Thought Machine

arXiv:2610.19220v1 Announce Type: cross Abstract: The Continuous Thought Machine (CTM) is a recurrent network that attends to inputs and makes predictions based on the synchronization between its neur

Right Answer, Wrong Reason: Accuracy, Consistency, and Consensus Are Misleading Indicators of LLM Faithfulness in Clinical Decision Support

arXiv:2609.32817v2 Announce Type: replace Abstract: Clinical Large Language Models (LLMs) achieve strong medical-exam accuracy; however, a correct answer does not guarantee that the explanation names

RingNav: Task-Shaped Circular Memory for Visual Heading

model-releasesarxiv-cs-cv

arXiv:2610.19044v1 Announce Type: cross Abstract: Tiny robots have limited computation and memory, yet visual navigation systems often maintain more state than a task requires. We ask whether persiste

RINO: Renormalization Group Distillation with No Labels

arXiv:2610.15919v1 Announce Type: cross Abstract: Supervised machine learning in high-energy physics (HEP) requires training on labeled simulations of particle collisions, which are known to be unfait

researcharxiv-cs-ai

Risk-Controlled DFT Selection for Machine-Learned Interatomic Potentials

arXiv:2610.19630v1 Announce Type: cross Abstract: Automated materials discovery requires evaluating large pools of candidate configurations, with density functional theory (DFT) as a major throughput

Risky-Bench: Evaluating Agentic Safety Risks in Real-World Life-Assist Deployments

arXiv:2602.03100v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly deployed as agents that operate in real-world environments, introducing safety risks beyond linguistic

RL Post-Training Diffusion Language Models Along Their Own Generation Paths

arXiv:2610.17266v1 Announce Type: new Abstract: Diffusion language models are a promising and more efficient alternative to autoregressive models. However, most RL post-training techniques are not eas

RM-OPD: On-Policy Distillation for Generative Reward Models

arXiv:2610.20174v1 Announce Type: cross Abstract: Reinforcement Learning on Mixed-domain preference data (Mix-RL) has become the mainstream approach for training general-purpose Generative Reward Mode

safetyarxiv-cs-cl

ROAR: Unifying Runs across Heterogeneous AI-Driven Research Systems

arXiv:2610.03966v2 Announce Type: replace Abstract: Each run of an AI-driven research system (ADRS) is an expensive search over a vast solution space, and dependable evaluation requires many runs, mak

RoboSpan: Learning Modular Coordination Policies for High-DoF Whole-Body Mobile Manipulation with Teleoperation

arXiv:2610.19594v1 Announce Type: new Abstract: Whole-body mobile manipulation suffers from two fundamental challenges: inherent motion heterogeneity across locomotion, arm, and hand modules, and prog

researcharxiv-cs-ro

Robota-Embed: Unified VLA Embeddings for Robot In-Context Learning and Evolution

model-releasesarxiv-cs-cv

arXiv:2610.19495v1 Announce Type: cross Abstract: Retrieving useful context for a robot requires matching its task, execution stage, and the continuation that should follow. We introduce Robota-Embed,

Robust Explanatory Analysis for Structured Optimization Networks (REASON) in Learning

arXiv:2610.19378v1 Announce Type: cross Abstract: We seek a hierarchical resolution of residual error under a composite learning objective as a principled mechanism for identifying and exploiting addi

Robust Real-Time Monocular Human Pose Estimation with Insole-Based Sensor Fusion

arXiv:2610.19494v1 Announce Type: new Abstract: Real-time monocular human pose estimation is an important sensing component for human-centered intelligent systems. While recent camera-based methods ha

“rogue” hacks are just a special case of a much larger problem: empowering systems that are poor at instruction following, coupled with a la…

“rogue” hacks are just a special case of a much larger problem: empowering systems that are poor at instruction following, coupled with a lack of common sense. AI agent deleted entire upcoming season

agentsgary-marcus--x

Tutorials

ROI-Reasoning: Rational Optimization for Inference via Pre-Computation Meta-Cognition

arXiv:2601.03822v2 Announce Type: replace Abstract: Large language models (LLMs) can achieve strong reasoning performance with sufficient computation, but they do not inherently know how much computat

tutorialsarxiv-cs-ai

RoTA-Bench: Evaluating 3D Spatial Reasoning through Interactive Projection Alignment

arXiv:2610.17665v2 Announce Type: new Abstract: Can vision-language models turn 2D observations into 3D understanding that supports precise action? This requires inferring object geometry, anticipatin

RouteWeaver: Weaving Mode Selection and Execution into Unified LLM Routing

arXiv:2610.19705v1 Announce Type: new Abstract: LLM routing dynamically selects and coordinates heterogeneous language models across diverse tasks. However, existing approaches are largely built aroun

safetyarxiv-cs-cl

RTAformer: Relation-Transition-Aware Transformer for Video Scene Graph Generation

arXiv:2610.20091v1 Announce Type: new Abstract: Video scene graph generation (VidSGG) aims to capture objects and their evolving relationships in videos. This requires accounting for both the persiste

safetyarxiv-cs-cv

RUBATO: Decoupled Clocks Allow Full-Duplex Models to Think as Fast as They Can

arXiv:2610.14299v1 Announce Type: cross Abstract: Recent Speech Full-Duplex (FDX) models are capable of engaging in naturalistic conversation by listening and speaking at the same time. Building on th

researcharxiv-cs-ai

Rule-based and reinforcement-learning policies for resource allocation in a pediatric echo lab: a simulation study

arXiv:2506.06297v2 Announce Type: replace Abstract: Scheduling echocardiographic examinations in a hospital setting presents significant challenges due to non-deterministic factors such as patient no-

safetyarxiv-cs-lg

Running Fewer Loops Removes Refusal Behaviour in Recurrent-Depth Language Models

arXiv:2610.15218v1 Announce Type: new Abstract: A recurrent-depth language model reuses one block of weights several times in a row. The number of repetitions, which we call loops, is chosen when the

S^3-Nav: Structured Spatial-Semantic Decision Making for Language-Guided Robot Navigation

arXiv:2610.19431v1 Announce Type: new Abstract: This paper presents S^3-Nav, a structured spatial-semantic decision-making framework for language-guided robot navigation under partial observability. R

safetyarxiv-cs-ro

Safe-FCL: Continual Risk Awareness and Fine-Grained Safety Aggregation for Federated Continual Learning of LLMs

arXiv:2610.19968v1 Announce Type: new Abstract: Federated fine-tuning enables large language models (LLMs) to learn collaboratively from distributed client data, but malicious client updates can compr

safetyarxiv-cs-lg

Safe Reinforcement Learning with Receding-Horizon Safety Critic and Differentiable Action Shields

arXiv:2610.20255v1 Announce Type: new Abstract: Despite the Markov assumption, safety failures in robot reinforcement learning rarely occur instantaneously; they typically emerge over several steps as

safetyarxiv-cs-ro

Local Ai

SafeAdapt-Poker: Counterfactual Risk Calibration for Budgeted Adaptation under Opponent Shift

arXiv:2610.20473v1 Announce Type: cross Abstract: Opponent histories can mislead after behavioral shifts. SafeAdapt-Poker formulates budgeted adaptation under opponent shift as a dual-counterfactual s

local-aiarxiv-cs-lg

SafeDriveVLA: Navigation-Conditioned World Model Dreaming for Conflict-Aware End-to-End Autonomous Driving

model-releasesarxiv-cs-ro

arXiv:2610.20609v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models are gaining attention in autonomous driving, inheriting commonsense priors from large language models (LLMs) and wid

SafeDSE: Joint Exploration of Safety Architecture Patterns, Mapping, and Scheduling for Dataflow Applications With Safety Arguments (Extended Version)

arXiv:2610.16213v1 Announce Type: cross Abstract: Embedded platforms increasingly host safety-related streaming functions next to less critical ones. Whether a safety architecture pattern, such as a d

Safety for Whom? Benchmarking Understanding Beyond Refusal in Egocentric Multimodal Assistants

arXiv:2610.17728v1 Announce Type: new Abstract: Egocentric multimodal assistants see through the user's eyes and guide what the user does next, so their outputs directly shape physical actions, includ

SAGE: Efficient Protected Vision-Language Inference on Smart Glasses

arXiv:2610.14089v1 Announce Type: cross Abstract: Smart glasses are a promising platform for vision-language assistants, but private split inference remains expensive: protecting visual representation

SAGE: Pushing 2-Bit Vector-Quantized LLMs toward Real-World Usability

arXiv:2610.14745v1 Announce Type: new Abstract: Extreme low-bit weight-only quantization is an effective approach to reducing the deployment costs of large language models. Below 3 bits, scalar quanti