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The Platonic Universe: Do Foundation Models See the Same Sky?

arXiv:2509.19453v2 Announce Type: replace-cross Abstract: We investigate when foundation models converge towards shared representations, and how this convergence depends on model capacity, training re

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The Statistical Benefits of Multiple Responses for Learning from Demonstrations

DGX agent

arXiv:2609.33291v1 Announce Type: cross Abstract: Many generative systems return multiple candidate responses and are evaluated according to the best one. Recent work shows that, when demonstrations a

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29 Sept 2026

Think Fast, Plan Selectively: Adaptive Deliberation for Efficient Data-Driven MPC

arXiv:2609.32591v1 Announce Type: cross Abstract: Data-driven model predictive control (MPC) combines learned world models with online trajectory optimization, achieving strong performance in continuo

TIDE: Teacher-Student Transition via Informative Distillation and Exploration for Agentic RL

arXiv:2609.35058v1 Announce Type: new Abstract: Effective multi-turn agents require interaction strategies that coordinate information gathering, actions, and feedback over long horizons. GRPO is a re

Timed Rule-Based Supervision of an End-to-End Autonomous Parking Policy

arXiv:2609.31773v1 Announce Type: new Abstract: We study whether a manually specified runtime supervisor can correct recurring failures of an existing end-to-end parking policy in a fixed CARLA parkin

TimelyDAgger: Timing-Aware Expert Querying for VLA Policy Improvement

arXiv:2609.33157v1 Announce Type: new Abstract: DAgger improves robot policies by aggregating expert supervision from states visited during policy execution. Robot-gated DAgger automates expert querie

Timestep Weighting: A Hidden Key to Effective ELBO-Based Flow-Matching RL

arXiv:2609.32665v1 Announce Type: cross Abstract: ELBO-based reinforcement learning offers a sampler-agnostic approach to fine-tuning flow matching models with reward feedback. Timestep weighting in E

TokenCast: Forecasting Token Consumption During LLM Agent Execution

arXiv:2609.35760v1 Announce Type: new Abstract: When a large language model (LLM) agent executes the same task, token consumption can vary by over an order of magnitude across runs. The agent chooses

TRACE: Trajectory-Based Safety Patch Learning for LLM Post-Training Realignment

arXiv:2607.16242v2 Announce Type: replace Abstract: Fine-Tuning-as-a-Service (FTaaS) platforms let users perform supervised fine-tuning (SFT) on customized data, but this pipeline can erode model safe

Train Together or Merge Later? Unifying VLA Experts via a Shared Action Interface

arXiv:2609.33125v1 Announce Type: new Abstract: Co-training offers a straightforward way to build a multi-task vision-language-action (VLA) policy, but can fall short of the performance achieved by tr

Trajectory-Safe Orienteering for Human-Robot Shared Environments

arXiv:2609.34485v1 Announce Type: new Abstract: Orienteering problem (OP) has wide real-world applications and also great potential in human-robot collaboration. However, existing approaches struggle

Transfer Learning for Edge Classification on Dynamic Text-Attributed Graphs

arXiv:2609.32849v1 Announce Type: new Abstract: Learning transferable representations for dynamic text-attributed graphs (DyTAGs) requires models to capture underlying interaction dynamics that persis

Trump orders US government to call AI ‘Super Intelligence’

The US executive branch is no longer acknowledging the existence of 'artificial intelligence.' Going forward, official policy websites, policy documents, and press releases will refer only to 'Super I

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TSGate: Timestep-Aware Gated Attention for Diffusion Transformers

arXiv:2609.34539v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) have emerged as the dominant architecture for high-fidelity image and video generation. Recent DiT systems increasingly us

TTRSD: Test-Time Reinforcement Learning with Self-Distillation for Vision-Language Models

arXiv:2609.33414v1 Announce Type: new Abstract: Test-time reinforcement learning enables vision-language models (VLMs) to adapt using unlabeled inputs. However, repeated sampling under fixed visual co

Twist, Don't Tilt: Trajectory-Exact Constrained Decoding for Masked Diffusion Models

arXiv:2609.35609v1 Announce Type: new Abstract: Constrained decoding for Masked Diffusion Language Models (MDLMs) aims to ensure that generated outputs satisfy a specified structure or syntax constrai

Two-Stage Learned Decomposition for Scalable Routing on Multigraphs

arXiv:2605.05389v2 Announce Type: replace Abstract: Most neural methods for Vehicle Routing Problems (VRPs) are limited to Euclidean settings or simple graphs. In this work, we instead consider multig

Two-Timescale Fine-tuning Provably Learns New Features for Two-Layer ReLU Networks

arXiv:2609.34667v1 Announce Type: cross Abstract: Fine-tuning pre-trained models on specialized tasks with scarce data is central to modern deep learning. Despite its empirical success, theoretical un

Unbiased Top-k Estimation for On-Policy Distillation

arXiv:2609.34447v1 Announce Type: cross Abstract: On-policy distillation (OPD) is becoming an important component of large language model (LLM) post-training for transferring the reasoning capability

Uncertainty Quantification of Next Generation Reservoir Computing with Applications to Memory-Driven Dynamical Systems

arXiv:2609.32169v1 Announce Type: cross Abstract: Nonlinear dynamical systems with memory arise across science and engineering, yet uncertainty quantification for efficient forecasting methods such as

Uncovering Ordinal-Matching Bias in Audio-Visual LLMs

arXiv:2609.34223v1 Announce Type: new Abstract: This work aims to improve how audio-visual large language models (AVLLMs) associate speech with the correct visible speaker in multi-speaker scenes. We

Understanding and inverse design of implicit bias in stochastic learning: a geometric perspective

arXiv:2601.06597v3 Announce Type: replace Abstract: Can we design a model such that its stochastic training favours a desired class of solutions without enforcing an explicit penalty? Under suitable c

Understanding Generalization Requires Universal Induction

arXiv:2609.34458v1 Announce Type: cross Abstract: Classical statistical theory is insufficient to explain the successes of general-purpose AI models, because it depends on handcrafted inductive biases

UniCache: Task- and Type-Aware KV Cache Compression for Unified Multimodal Models

arXiv:2609.32831v1 Announce Type: new Abstract: Unified multimodal models combine understanding, generation, and editing within a single network, offering a promising foundation for versatile multimod

Unified Trajectory Matching Policy Optimization: Diverse T2I Generation and VLA Generalization

arXiv:2609.34688v1 Announce Type: new Abstract: Reward-maximizing reinforcement learning (RL) is widely used to post-train stochastic diffusion and flow policies for text-to-image (T2I) generation. Ho

UOPD: Uncertainty-Aware Intervention for On-Policy Distillation of Multi-Turn Agents

arXiv:2609.34036v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student on its own rollouts using dense supervision from a teacher. In multi-turn environments, a mistake at a cri

Vanilla Policy Optimization Is Both Optimal and Differentially Private for Stochastic Contextual Bandits

arXiv:2609.33888v1 Announce Type: new Abstract: Can vanilla policy optimization explore enough to achieve near-optimal regret in stochastic contextual bandits? We show that standard exponential policy

Verifying Neural Networks with Reinforcement Learning

arXiv:2609.34553v1 Announce Type: new Abstract: Formal verification can play a key role in ensuring the reliability of Deep Neural Networks (DNNs) deployed in safety-critical systems. Modern DNN verif

Verifying the Linear Representation Hypothesis: How Interpretable Are Vision SAEs?

arXiv:2609.35020v1 Announce Type: new Abstract: Vision Sparse Autoencoders (SAEs) have become a popular tool in Mechanistic Interpretability due to their presumed ability to disentangle complex featur

Vision--Language Signals in Constrained RL: Safety Gains Without Anticipation

arXiv:2609.34041v1 Announce Type: new Abstract: Safe reinforcement learning seeks policies that maximise task performance while satisfying safety constraints. In driving benchmarks, however, collision

WALA Learning Executable Latent Actions from Action-Labeled Demonstrations and Action-Free Videos

arXiv:2607.11397v2 Announce Type: replace Abstract: Human videos provide rich information about object manipulation at a scale difficult to reproduce with robots, but often lack action annotations tha

WAM-OPD: Sharpening World Action Models via On-Policy Distillation

arXiv:2609.34250v1 Announce Type: new Abstract: Pretrained world action models (WAMs) provide generalist capabilities across diverse robotic manipulation tasks, yet improving target-task performance t

Watch the Model Think: On-Policy Extraction of Activation Steering Vectors

arXiv:2602.14143v2 Announce Type: replace Abstract: When a model solves a problem on one attempt and fails it on the next, what separates the two is rarely the final answer token; it is the trajectory

Wavelet-Based Parity Detection Revisited: Representation Dependence, Generalization, and Mechanistic Analysis

arXiv:2511.00071v2 Announce Type: replace Abstract: Parity is exactly determined by the least significant bit (LSB), so machine learning is unnecessary for solving the task. This work instead uses par

Weighted Spline-Expanded Networks with Distributional Balancing for Continuous Treatment Effects

arXiv:2609.33740v1 Announce Type: cross Abstract: Estimating causal effects with continuous treatments in observational studies is challenging due to confounding, model misspecification, and high-dime

What Does a ProcGen Generalization Gap Measure? Action Rules, Convergence, and the Missing Random Floor

arXiv:2609.32532v1 Announce Type: new Abstract: A generalization gap in reinforcement learning (return on training levels minus return on held-out levels) is usually reported without a reference point

What Does the Rank Buy? A Spectral and Distributional Analysis of Low-Rank Adaptation

arXiv:2609.32002v1 Announce Type: cross Abstract: The rank r in LoRA is widely treated as a capacity control: a smaller rank is assumed to yield a simpler model that generalizes better. We show that,

What Paired Evaluations Reveal under Visual Perturbations

arXiv:2609.35583v1 Announce Type: new Abstract: Robustness evaluation must examine diverse visual perturbations, while benchmarks cover only some real-world conditions and physical testing is costly.

What Should the Reflector See? An Empirical Study of Evidence in Reflective Prompt Optimization

arXiv:2609.32452v1 Announce Type: cross Abstract: Reflective prompt optimization revises instructions using examples of a model's behavior, but which evidence the reflector should receive remains uncl

What Visual Generators Need from Teachers: Rethinking Representation Alignment

arXiv:2609.34732v1 Announce Type: new Abstract: Representation alignment speeds up diffusion transformer training by pulling an intermediate block of the model (student) toward features of a frozen pr

When Better Gets Worse: Improvement Fidelity for Self-Improving Agents in Adaptive Worlds

arXiv:2609.32677v1 Announce Type: cross Abstract: Self-improving agents increasingly rely on proxy verifiers to choose policy updates, yet deployment can change the world in which those updates are ev

When Do Model Internals Help? Exploring the Role of Representation Engineering in LLM Safety

arXiv:2609.34771v1 Announce Type: cross Abstract: Reliable AI safeguards require both control mechanisms that reduce unsafe behavior and monitoring mechanisms that detect safety risks during model int

When Do Models Admit They Are Wrong? Failure Disclosure Is Unstable Under Reinforcement Learning

arXiv:2609.33220v1 Announce Type: new Abstract: Outcome-based reinforcement learning can produce models with similar task performance but very different ways of communicating about their mistakes. We

When do Random Forests work?

arXiv:2504.12860v2 Announce Type: replace-cross Abstract: We study the effectiveness of randomizing split-directions in random forests. Prior literature has shown that, randomization can reduce varian

When Does Dense Retrieval Need Asymmetric Geometry? A Bias-Variance Theory of Shared and Dual Projections

arXiv:2609.32488v1 Announce Type: cross Abstract: Dense retrieval powers retrieval-augmented generation, semantic search, and question answering, yet the theoretical basis for choosing between shared

When Does Synergy Help Active Feature Acquisition? A PID-Based Study

arXiv:2609.32301v1 Announce Type: new Abstract: Active feature acquisition (AFA) sequentially selects informative features under budget constraints. However, existing policies rarely distinguish wheth

When Helpful Text Hurts: Option-Redirecting Bias in Vision-Language Models

arXiv:2609.32489v1 Announce Type: cross Abstract: In tri-modal visual question answering (VQA), auxiliary text is commonly used to complement visual and textual inputs, yet its reliability is often un

When Known Physics Helps Neural PDE Models: Residual Constraints Out-Regularize Generic Priors for Nonlinear Dynamics

arXiv:2609.34012v1 Announce Type: new Abstract: Neural PDE surrogates increasingly incorporate structural priors, yet it is often unclear whether their gains arise from physics-specific information or