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Synergistic Modality-and-Slice Memory Framework for Cross-Modal 3D Brain Tumor Segmentation

arXiv:2510.10679v2 Announce Type: replace Abstract: The 3D multi-modal brain tumor segmentation is critical to multi-modal healthcare, and it requires accurate identification of distinct internal anat

agentsarxiv-cs-cv

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TacForcing: Streaming Action Generation with Execution-Time Tactile Feedback

DGX agent

arXiv:2608.25798v1 Announce Type: cross Abstract: Contact-rich manipulation requires adapting to contact states that can evolve substantially within an action horizon. However, chunk-based vision-lang

applicationsarxiv-cs-lg

27 Aug 2026

TailSFT: Filtered Fine-Tuning Improves Post-Training Performance

arXiv:2608.25756v1 Announce Type: new Abstract: Reinforcement learning post-training drives reasoning and agentic capabilities in modern AI systems, yet a growing body of work shows that it is most ef

agentsarxiv-cs-lg

Targeting the Attention Heads Behind Object Hallucination in LLaVA

arXiv:2608.24966v1 Announce Type: new Abstract: Vision-language models such as LLaVA-1.5-7B often hallucinate objects absent from the image when generating captions. We ask whether an interpretability

tutorialsarxiv-cs-cv

TAU-Agent: An Agentic Retrieval-Augmented Framework for Traffic Anomaly Understanding

arXiv:2608.25935v1 Announce Type: new Abstract: Traffic Anomaly Understanding (TAU) requires models and systems to detect, reason about, and explain anomalous events in transportation videos. To addre

agentsarxiv-cs-cv

TDFNet: Tri-projection Deformable Fusion Network for Panoramic Salient Object Detection

arXiv:2608.25808v1 Announce Type: new Abstract: Recent years have witnessed the growing potential of panoramic salient object detection in robotic vision, virtual reality, and related applications. Ho

Local Ai

THA-Flow Generative Model: Prosthesis Geometry Prediction from Preoperative CT

arXiv:2608.25845v1 Announce Type: new Abstract: Preoperative planning for total hip arthroplasty (THA) is commonly framed as selecting a single prosthesis configuration and placement for a patient's o

local-aiarxiv-cs-cv

The Changing Geometry of Grammar: Dimensionality and Neighborhood Reorganization across Transformer Layers

arXiv:2608.25166v1 Announce Type: new Abstract: Transformer representations describe trajectories through high-dimensional vector spaces, which are shaped dynamically as tokens incorporate relational

The 'Curse of Knowledge' in LLM Query Simulation: Concept Provenance for Tracing Answer-Side Intrusion

arXiv:2608.25245v1 Announce Type: cross Abstract: LLM-generated search queries are widely used to augment IR evaluation, yet they may contain concepts that presuppose answer-side document knowledge, v

researcharxiv-cs-cl

The Dialect Tax: Dialectal Biases Persist throughout the Language Modeling Pipeline

arXiv:2608.24952v1 Announce Type: new Abstract: Systematic dialectal performance gaps in language models (LMs) are well documented, but the source of these disparities within the modern language model

tutorialsarxiv-cs-cl

The Frame Kernel Method for Multiscale Operator Learning

arXiv:2608.25084v1 Announce Type: new Abstract: We present a natively multiscale operator learning method for the surrogate modeling of (numerical solvers for) multiscale partial differential equation

The Imperfective Paradox Is Not Necessarily in Large Language Models: A Benchmark Failure Before a Model Failure

arXiv:2608.25005v1 Announce Type: new Abstract: The imperfective paradox provides a useful test of compositional semantic analysis. Recent work constructs an NLI benchmark and reports that models freq

The Von-Neumann State-Space Transformer for neural decoding

model-releasesarxiv-cs-lg

arXiv:2608.25088v1 Announce Type: new Abstract: Cortical computation is strikingly low-dimensional: a handful of latent variables, carried in a neural population's activity, steer the higher-dimension

Theoretically Principled Federated Learning for Balancing Privacy and Utility

model-releasesarxiv-cs-lg

arXiv:2305.15148v3 Announce Type: replace Abstract: We propose a general learning framework for the protection mechanisms that protects privacy via distorting model parameters, which facilitates the t

Thermodynamic cost of inference and learning in physical neural networks

arXiv:2503.09980v4 Announce Type: replace-cross Abstract: How much of the energy consumed by artificial neural networks is set by physics rather than by implementation? For irreversible digital hardwa

safetyarxiv-cs-lg

Think-Probe-Respond: Improving Large Language Models as Judges of Research Idea Novelty

arXiv:2608.25660v1 Announce Type: new Abstract: Automated novelty judgment can accelerate scientific discovery by enabling efficient evaluation, refinement, and comparison of research ideas. While lar

safetyarxiv-cs-cl

Three-Way Open-Set Detection for Robust Autonomous Navigation

arXiv:2511.15343v2 Announce Type: replace Abstract: Autonomous navigation in complex scenes requires reliable perception across scenarios that the model did not encounter during its training. Along it

agentsarxiv-cs-cv

Token-Oriented Semantic Communication with Pretrained Vision Transformers

arXiv:2608.25410v1 Announce Type: cross Abstract: Token communications realize the semantic communication principle at the granularity of transformer tokens, providing a promising direction for client

Tool Verification for Test-Time Reinforcement Learning

arXiv:2603.02203v2 Announce Type: replace-cross Abstract: Test-time reinforcement learning (TTRL) has emerged as a promising paradigm for Recursive Self-Improving AI (RSI) by adapting Large Reasoning

researcharxiv-cs-cl

Hardware

TOPAS: Workflow-Aware Prefix-State Scheduling for Multi-Agent LLM Serving

arXiv:2608.25523v1 Announce Type: new Abstract: Prefix caching introduces a fundamental tradeoff in multi-agent large language model (LLM) serving: retaining a long system-prompt key-value (KV) cache

hardwarearxiv-cs-cl

Toward Machine Learning with the Unit as a Primitive: Learning from Unit-Linked Events

arXiv:2608.25118v1 Announce Type: new Abstract: Machine learning is usually formalized through samples, while the persistent individual to which multiple observed or possible events refer often remain

Applications

Towards A Unified Information Bottleneck Framework for Time Series Explanations

arXiv:2608.25897v1 Announce Type: new Abstract: Explaining deep learning models operating on time series data is crucial in various applications that require transparent and interpretable insights int

applicationsarxiv-cs-lg

Towards Fine-Grained Text-to-3D Quality Assessment: A Benchmark and A Two-Stage Rank-Learning Metric

model-releasesarxiv-cs-cv

arXiv:2509.23841v3 Announce Type: replace Abstract: Recent advances in Text-to-3D (T23D) generative models have enabled the synthesis of diverse, high-fidelity 3D assets from textual prompts. However,

Towards Purified Multi-Label Test-Time Adaptation of Vision-Language Models

arXiv:2608.25653v1 Announce Type: new Abstract: Test-time adaptation (TTA) has been widely explored in single-label recognition, effectively mitigating distribution shifts, especially when combined wi

Towards Reliable, Generalizable, and Specific In-Context Knowledge Editing via Multi-Objective Reinforcement Learning

model-releasesarxiv-cs-lg

arXiv:2608.25100v1 Announce Type: cross Abstract: Large Language Models (LLMs) are powerful but limited by static parametric knowledge that becomes outdated once pretraining ends. Knowledge editing ad

Towards safe and optimal flight: Viability Kernel MPC for Fully Actuated Multirotor

arXiv:2608.25459v1 Announce Type: new Abstract: Industrial aerial robotics demands safety guarantees for navigation in unstructured environments while optimizing performance and computational efficien

safetyarxiv-cs-ro

Trace Integrity for LLM Data Agents: A Vision for Auditable Structured Reasoning in Real-World Systems

arXiv:2608.26036v1 Announce Type: cross Abstract: Answer accuracy is an insufficient reliability signal for LLM data agents. In structured-data tasks, a benchmark-correct answer can be produced by an

TraceML: An Empirical Analysis of Human-Agent Planning in Machine Learning Development

arXiv:2608.26086v1 Announce Type: new Abstract: Large language models write correct code for isolated problems but remain far weaker at autonomous machine-learning development, where an agent must rev

agentsarxiv-cs-lg

Training Alignment Auditors via Reinforcement Learning

arXiv:2608.25460v1 Announce Type: cross Abstract: Alignment auditing of frontier models increasingly relies on LLM auditors to surface undesirable behaviors at scale, but current automated auditors ca

safetyarxiv-cs-lg

Training-Free Interaction-Aligned Visual Token Pruning for Efficient Embodied Manipulation

arXiv:2603.22991v2 Announce Type: replace Abstract: Efficient visual representation is a central image-processing challenge in embodied manipulation, where policies repeatedly process dense visual-tok

Transforms for LLM Quantization: The Great Inversion and Format Co-Design

arXiv:2608.25188v1 Announce Type: new Abstract: Most competitive 4-bit LLM research pipelines now open the same way: apply a linear, function-preserving transform (rotation, scaling, permutation, non-

tutorialsarxiv-cs-lg

Local Ai

Transient multimode heat transfer of an industrial automated tape laying process under rapidly changing conditions

arXiv:2608.25470v1 Announce Type: new Abstract: This work presents a transient heat-transfer model of an industrial automated tape laying (ATL) process designed to overcome the limitations of conventi

local-aiarxiv-cs-ro

Applications

Trojan Attacks on Neural Network Controllers for Robotic Systems

arXiv:2602.05121v3 Announce Type: replace-cross Abstract: Neural network controllers are increasingly deployed in robotic systems for tasks such as trajectory tracking and pose stabilization. However,

applicationsarxiv-cs-ro

Tropospheric temperature and humidity profile retrieval from Meteosat Flexible Combined Imager based on deep learning

arXiv:2608.25700v1 Announce Type: new Abstract: The Meteosat Third Generation (MTG) Flexible Combined Imager (FCI) offers new opportunities for tropospheric temperature and humidity profiling, at high

agentsarxiv-cs-lg

Local Ai

Trust-Aware Sequential Decision Making and Rollout Planning for Resilient Multi-Robot Systems

arXiv:2608.25690v1 Announce Type: cross Abstract: Sequential decision-making in multi-robot systems typically assumes that planning information is reliable and that agents execute the actions anticipa

local-aiarxiv-cs-ro

Trust the Mass: Forced Weights in KV-Cache Eviction

arXiv:2608.25230v1 Announce Type: cross Abstract: Every deployed sparse-attention or KV-cache-eviction rule keeps a subset of the keys, discards the rest, and renormalizes the attention weights over t

TrustFormer: Cross-Temporal and Cross- Dimensional Transformer for Task-Specific Multi-Dimensional Trust Evaluation

arXiv:2608.25238v1 Announce Type: cross Abstract: In dynamic collaborative systems, the selection of reliable collaborators is critical to ensuring effective task execution. Existing trust evaluation

TTSR: Test-Time Self-Evolving via Reflection

arXiv:2603.03297v2 Announce Type: replace Abstract: Test-time training (TTT) adapts large language models (LLMs) during inference using only unlabeled test inputs. Existing methods, however, face two

tutorialsarxiv-cs-cl

TurnBench: A Multi-Domain Benchmark for Turn-Taking Dynamics in Spoken Dialogue

arXiv:2608.25218v1 Announce Type: cross Abstract: Speakers in natural conversation take turns speaking and listening, deciding in real time when to take, hold, or yield the floor. However, turn-taking

Tutorial on Aided Inertial Navigation Systems: A Modern Treatment Using Lie-Group Theoretical Methods

arXiv:2603.07143v2 Announce Type: replace Abstract: This tutorial presents a control-oriented introduction to aided inertial navigation systems using a Lie-group formulation centered on the extended S

tutorialsarxiv-cs-ro

Two Dimensions Govern Agnostic Multiclass Transductive Learning

arXiv:2608.25326v1 Announce Type: new Abstract: In transductive classification, an adversary fixes a labeled population, one label is hidden uniformly, and the learner sees all remaining labels. For b

UltraPIPS: Improving model perception in B-mode ultrasound with foundation models

arXiv:2608.26033v1 Announce Type: new Abstract: In medical imaging, it is common to use learned perceptual image patch similarity (LPIPS) to compare images semantically in feature space. Although back

researcharxiv-cs-cv

Uncertainty-Guided Latent Diffusion Models for Faithful Super Resolution

arXiv:2608.25998v1 Announce Type: new Abstract: The perception-distortion trade-off poses a fundamental challenge in single-image super-resolution (SR). Although diffusion-based SR methods excel at ge

tutorialsarxiv-cs-cv

Hardware

Understanding the Energy Scaling of Large Language Model Inference Across Context Lengths and Attention Architectures

arXiv:2608.25096v1 Announce Type: new Abstract: The growing adoption of large language models (LLMs) has raised increasing concerns about the energy consumption and environmental impact of inference.

hardwarearxiv-cs-lg

Unfolding Scientific Papers into Multi-Turn Generation Trajectories for Continued Pre-Training

arXiv:2608.25826v1 Announce Type: new Abstract: A recent line of synthetic-data work reconstructs the thinking behind existing text rather than rewriting the text itself, but it operates on short web

Unmatched Does Not Mean False: Incomplete Reference Sets Can Reverse Calibration Rankings in Open-Ended Theory-of-Mind Tracking

arXiv:2608.25654v1 Announce Type: new Abstract: Open-ended Theory-of-Mind (ToM) trackers emit valid beliefs absent from finite references. A finite-reference-plus-matcher pipeline marks unmatched outp

Unsupervised Anatomical Feature Learning via Diffusion Models: Enhanced Medical Image Segmentation with Denoising Diffusion Probabilistic Models

arXiv:2608.25693v1 Announce Type: new Abstract: Acquiring pixel-level annotations for medical image segmentation is a severe bottleneck. Traditional U-Net architectures, while effective, learn local t

tutorialsarxiv-cs-cv

Unsupervised Post-Training of Foundation Models: A Survey

arXiv:2608.24982v1 Announce Type: new Abstract: Foundation-model post-training usually relies on human labels, preference data, stronger teachers, or executable verifiers. We study Unsupervised Post-T

researcharxiv-cs-cl