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arXiv:2609.33937v1 Announce Type: new Abstract: Test-Time Adaptation (TTA) and Generalized Category Discovery (GCD) are traditionally treated as disjoint problems: the former adapts models to domain s

applicationsarxiv-cs-cv

Test-Time Spatial Reasoning for Robot Manipulation Using Generative Real-to-Sim

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DGX agent

arXiv:2609.33982v1 Announce Type: cross Abstract: Spatial reasoning is fundamental to general robot intelligence, as it enables robots to complete long-horizon tasks involving multi-object interaction

model-releasesarxiv-cs-cv

29 Sept 2026

Tetra: Serving Leech-Lattice Quantized LLMs at 2.7 Bits per Parameter

arXiv:2609.35465v1 Announce Type: new Abstract: Leech-lattice quantization gives good quality at two bits per weight, but its codebooks hold more than 10^14 points, too many for a lookup table. Our ea

Text-Vision Synergistic Token Caching: A Training-Free Framework for Efficient Vision-Language-Action Inference

arXiv:2609.34319v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models enable generalizable robotic control but remain computationally expensive. Token caching provides a training-free, p

safetyarxiv-cs-cv

TGRL: Temperature-Grouped Reinforcement Learning for Efficient Exploration in LLMs

arXiv:2609.33589v1 Announce Type: new Abstract: Efficient exploration often remains a central bottleneck in reinforcement learning with verifiable rewards (RLVR). Although temperature control and test

The Composition Gap in Dataset Distillation

arXiv:2609.34343v1 Announce Type: new Abstract: Dataset distillation compresses a training set into a small synthetic set, usually evaluated one at a time. In federated and data-governance settings, s

The cost of useful natural gradient updates

arXiv:2609.33499v1 Announce Type: new Abstract: What information is needed to turn a natural-gradient direction into a useful finite update? Under a population Kullback-Leibler (KL) budget, we call a

The Devil in the Details: Emergent Misalignment, Format and Coherence in Open-Weights LLMs

arXiv:2511.20104v2 Announce Type: replace Abstract: Prior work has shown that fine-tuning models on a narrow domain with misaligned data can lead to broad misalignment - a phenomenon termed 'emergent

The Devil is in the Spectrum Bias: Spectrum-Balanced Feature Matching for Robust Representation Distillation

arXiv:2609.34106v1 Announce Type: new Abstract: Large visual foundation models have demonstrated remarkable transferability across a wide range of downstream tasks. To deploy such models efficiently,

safetyarxiv-cs-cv

The Double-Edged Sword of Information: Revealed versus Hidden Lotteries in School Choice

arXiv:2609.34074v1 Announce Type: cross Abstract: In school choice, a lottery number is often used by the matching mechanism to break ties when there are more students who prefer the same school than

The Earth in One Gaze: Training-Free Active Focus for UHR Remote Sensing Understanding

arXiv:2609.31747v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) must balance local detail against scene context when interpreting ultra-high-resolution (UHR) remote sensing (R

The Exponentially Weighted Signature

arXiv:2603.19198v2 Announce Type: replace-cross Abstract: We introduce the exponentially weighted signature (EWS), a continuous-time model that computes iterated integrals of a path, where each increm

The Extender: A Log-Structured Transformer

arXiv:2609.32759v1 Announce Type: new Abstract: We introduce the Extender, a log-structured variant of the standard Transformer architecture. In a standard Transformer, each layer communicates with su

Tutorials

The Geometry of Logic: Stratification Induces Semantic Structure and Robust Reasoning

arXiv:2609.32927v1 Announce Type: new Abstract: Transformer-based language models perform well on symbolic tasks, yet it remains unclear whether they learn generalizable rules or rely on statistical s

tutorialsarxiv-cs-lg

The GUI Is Not the State: Diagnosing State Aliasing in GUI World Models

arXiv:2609.32679v1 Announce Type: cross Abstract: GUI World Models (GUI-WMs) are increasingly used to predict future states for agent planning and simulation, yet most existing formulations condition

Tutorials

The Hidden Perception Constraint in Task-Aware Compression

arXiv:2609.35684v1 Announce Type: cross Abstract: With the recent advancements of neural compressors, explicitly incorporating perception constraints into the design of compression schemes has gained

tutorialsarxiv-cs-lg

The Hidden Ratio in Adam: Stable Structure, Compression, and Sign Dynamics

arXiv:2609.35392v1 Announce Type: new Abstract: Adam is the default optimizer for training modern deep neural networks, yet its adaptive behavior remains poorly understood due to the complex interacti

The Impact of Stochasticity on the Rashomon Effect in Machine Learning

arXiv:2609.32934v1 Announce Type: new Abstract: Neural network training is inherently stochastic, with factors such as weight initialization leading to distinct models despite comparable predictive pe

The Judge Is Not Its Twin: Post-training makes a model's writing more predictable but barely moves its taste, as a judge, toward predictable writing

arXiv:2609.32196v1 Announce Type: cross Abstract: Language models are now routinely graded by other language models. If post-training makes a model's own writing more predictable, it may also teach th

The Key Handoff: Retrieval in Hybrid Language Models

arXiv:2609.32942v1 Announce Type: cross Abstract: A two-hop question makes a language model retrieve twice: once to produce a bridge entity, and once to retrieve with it. Transformers resolve that ent

The limits of exactness: On the failure of automatic differentiation in physics-informed machine learning

arXiv:2609.33078v1 Announce Type: new Abstract: Automatic differentiation (AD) lets neural networks compute derivatives of governing equations to machine precision, and this precision has made it the

The Low-Rank Structure of VLA Reinforcement Learning

arXiv:2609.34599v1 Announce Type: new Abstract: Reinforcement learning (RL) is increasingly used to post-train vision-language-action (VLA) models, yet how RL reshapes these policies remains poorly un

The Model Knows When to Stop: Training-Free Early Stopping for Long-Context Reading

arXiv:2609.34590v1 Announce Type: cross Abstract: Language models often process long inputs sequentially in chunks, but continuing to read after sufficient evidence has been acquired wastes computatio

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

The Price of Locality: Why Forward-Forward Underperforms Backpropagation?

arXiv:2609.33240v1 Announce Type: new Abstract: The Forward-Forward Algorithm (FFA) replaces backpropagation (BP) with layer-wise local contrastive objectives, eliminating the backward pass and the ne

The Price of Peeking: Anytime-Valid Leakage Detection on ML-KEM EM Traces

arXiv:2609.33597v1 Announce Type: cross Abstract: Side-channel evaluators routinely inspect leakage tests while acquisition is still running, and extend or stop the campaign based on what they see. Fi

The Privacy Fallacy of Crowdsourced Fine-Tuning: Extracting Proprietary Data via Topic-Based Poisoning

arXiv:2609.33985v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) is widely used to adapt large language models to downstream tasks. Crowdsourcing user conversations is an established app

The Selection Rule Decides the Winner: A Pre-Registered Audit of Open-Set Graph Anomaly Detection

arXiv:2609.33370v1 Announce Type: new Abstract: Open-set graph anomaly detection trains on a few labeled anomalies from one class and must also find anomaly classes that were never labeled. Published

The Statistical Benefits of Multiple Responses for Learning from Demonstrations

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

The Statistical Cost of Causal Discovery with Feedback

arXiv:2609.34050v1 Announce Type: cross Abstract: What determines the unavoidable sample cost of learning cyclic causal structure? For cyclic linear non-Gaussian models, we study exact condensation re

The Steering Budget: Examples beat Knobs

arXiv:2607.14246v2 Announce Type: replace-cross Abstract: Generative models are steered with knobs -- prompts, guidance scales, property tags. Turn one as hard as you like and, past a point, it stops

The Temporal Tug-of-War: Visualizing and Detecting RAG Conflicts in Diffusion Models via Trajectory Variance

arXiv:2609.31684v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) introduces a specific failure mode in discrete diffusion language models: when retrieved context contradicts para

The Text Beside the Image: Detection, Utility and Leakage for Trustworthy Multimodal Medical Data and Beyond

arXiv:2609.33280v1 Announce Type: cross Abstract: Medical images are released with the reports that describe them, and protecting the image does not protect the report. This paper measures the text co

THEIA: A Multimodal Dataset and Benchmark for Vision-Language Analysis of Layout

arXiv:2609.35035v1 Announce Type: new Abstract: The integration of artificial intelligence into computer-aided design frameworks has sparked a shift in the design of analog integrated circuits (ICs),

Theoretical Refinement of CLIP by Utilizing Linear Structure of Optimal Similarity

arXiv:2510.15508v2 Announce Type: replace Abstract: In this study, we propose an enhancement to the similarity computation mechanism in multimodal contrastive pretraining frameworks such as CLIP. Prio

Theory Guided and Interpretable Neural Operator Design for Partial Differential Equation Learning

arXiv:2609.33715v1 Announce Type: new Abstract: Accurate numerical solutions of partial differential equations (PDEs) are crucial in numerous science and engineering applications. In this work, we int

Theory of Scene: Breaking the Symmetry Trap in Multi-Agent LLM Coordination

arXiv:2609.32939v1 Announce Type: new Abstract: Multi-agent systems built on large language models (LLMs) are largely homogeneous, as their agents behave alike even across distinct LLMs. We show that

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

ThinkNet: Compact Architecture Selection and Validation-Gated Ensembles for Subject-Independent MI-EEG Decoding

arXiv:2609.33967v1 Announce Type: cross Abstract: Practical assistive and rehabilitative brain--computer interfaces require subject-independent motor-imagery EEG (MI-EEG) decoders that generalize to n

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

Tilted Schrodinger Bridge Matching

arXiv:2609.34642v1 Announce Type: new Abstract: Schrodinger bridges provide an entropy-regularized framework and a principled solution for unpaired domain translation. In practice, a pretrained bridge

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

safetyarxiv-cs-ro

Applications

TimeES: Probabilistic and Deterministic Time Series Forecasting via Evolutionary Spectra

arXiv:2609.32384v1 Announce Type: new Abstract: Real-world time series are inherently non-stationary, with trends, periodic patterns, and uncertainty evolving over time. While the Fourier domain offer

applicationsarxiv-cs-lg

Timeline-Bench: Evaluating Agents on Realistic Video-Editing Tasks, from Raw Footage to Final Cut

arXiv:2609.35143v1 Announce Type: new Abstract: AI agents increasingly carry out long-horizon professional work, but their evaluations rarely require a finished creative deliverable. To this end, we i

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

safetyarxiv-cs-ro

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

safetyarxiv-cs-cv

Local Ai

TLC-DiT: Task-Aligned Local Visual Conditioning for Robust Multitask Robot Manipulation

arXiv:2609.34297v1 Announce Type: new Abstract: Language-conditioned robot policies have made clear progress in multitask manipulation, but task-relevant local visual evidence usually stays hidden ins

local-aiarxiv-cs-ro

Agents

TMCS: Tool-Grounded Multi-Agent Reasoning for Compositional Chemical Problem Solving

arXiv:2609.35336v1 Announce Type: cross Abstract: Despite the promise of Large Language Models (LLMs) in computational chemistry, rigorous combinatorial chemistry problems remain difficult because the

agentsarxiv-cs-cv