All entries

Towards an AI Software Factory for Data Systems

arXiv:2609.36323v1 Announce Type: new Abstract: AI-assisted coding tools deliver significant acceleration of coding, but only limited impact across the end-to-end software development lifecycle (SDLC)

Towards Better Training Signal: Advantage Clipped Policy Optimization

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

arXiv:2609.36816v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a cornerstone for improving the reasoning capabilities of large language models (LLMs), but the need for on-polic

safetyarxiv-cs-lg

30 Sept 2026

Towards Breaking the Learning System Wall Using Multimodal Tutoring Transcriptions

arXiv:2609.36502v1 Announce Type: cross Abstract: Past research using log data has faced the 'learning system wall,' whereby few methods exist for generalizing models of student learning across platfo

Towards Mitigating Deceptive Safety Alignment in Large Reasoning Models

arXiv:2609.36254v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) are commonly trained with reinforcement learning (RL) to improve their generation of chain-of-thought (CoT) reasoning befo

Local Ai

Towards Scalable Context-Aware Single-Cell Spatial Transcriptomics Prediction from Histology Images

arXiv:2609.36429v1 Announce Type: new Abstract: Predicting gene expression from H&E-stained histology images offers a scalable alternative to costly spatial transcriptomics, yet most existing methods

local-aiarxiv-cs-cv

Towards Spatial Perception for Heterogeneous Robot Collaboration in Subterranean Mining Environments

arXiv:2609.37419v1 Announce Type: new Abstract: The autonomous extraction of deep mineral deposits in abandoned underground mines is fundamentally a multi-agent integration problem. No single platform

agentsarxiv-cs-ro

TRACE: Deployable Tree-Relational Structure Enhancement for Oncology LLMs

arXiv:2609.35810v1 Announce Type: cross Abstract: Large language models are increasingly used in oncology applications, but their predictions are often weakly grounded in explicit medical structure. W

Tracing mechanisms of sycophantic agreement in language models

arXiv:2609.35822v1 Announce Type: new Abstract: Sycophantic agreement in language models refers to the tendency to overly affirm a user's stated beliefs or preferences, often at the expense of factual

safetyarxiv-cs-cl

Tracing the Evidence: Faithful Token Attribution Through Vision-Language Reasoning

arXiv:2609.37656v1 Announce Type: new Abstract: Large vision-language models (LVLMs) exhibit strong reasoning capabilities, yet the visual and textual evidence supporting the generated responses remai

researcharxiv-cs-cv

Tracing the Evolution of Oracle Bone Characters Across Three Millennia

arXiv:2609.35674v2 Announce Type: replace Abstract: Of the approximately 4,500 Oracle Bone Inscription (OBI) characters discovered from the Shang dynasty, only about 1,600 have been deciphered. Many c

researcharxiv-cs-cl

Track-and-Complete: Learning Humanoid Skills from a Single Failed Human Video

arXiv:2609.36924v1 Announce Type: cross Abstract: Learning humanoid skills from videos typically requires a successful human demonstration, which often demands custom data collection. Although failure

safetyarxiv-cs-cv

Train Ahead, Distill Back: Bootstrapping On-Policy Self-Distillation for Large Language Models

arXiv:2609.37132v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) improves large language models by letting a self-teacher with privileged information provide dense token-level superv

Training-Free Global Geometric Association for 4D LiDAR Panoptic Segmentation

arXiv:2512.18991v3 Announce Type: replace-cross Abstract: Dominant paradigms for 4D LiDAR panoptic segmentation are usually required to train deep neural networks with large superimposed point clouds

Training LLMs to Verbalize Evaluation Awareness

arXiv:2609.36316v1 Announce Type: cross Abstract: Evaluation awareness (EA) can cause large language models (LLMs) to behave differently during audits than in deployment, yet measuring and accounting

Trajectory-Level Mode Guidance for Controllable Diffusion-Based Multi-Robot Motion Planning

arXiv:2609.36530v1 Announce Type: new Abstract: Motion planning often admits multiple feasible solutions, making multimodal generation valuable, particularly for flexible multi-robot coordination. Dif

agentsarxiv-cs-ro

Trajectory Soup: Pushing the Compute-Scaling Frontier of LLM Mid-training via Diverse Trajectories

arXiv:2609.37169v1 Announce Type: cross Abstract: Mid-training equips pretrained large language models with specialized and reasoning capabilities, but the returns of this stage are bounded since addi

Transformers Stop Thinking Too Early, and a Tiny LoRA Fixes It

arXiv:2609.36585v1 Announce Type: new Abstract: Pretrained transformers use little of their depth to follow references in context. Thirteen base models reliably follow only 1.4-3.6 lines, and extra pr

Transolver-sigma: Joint Spectral-Physical Subspace Modeling for Neural PDE Solving

arXiv:2609.37279v1 Announce Type: new Abstract: Neural solvers offer efficient surrogates for numerical simulation of partial differential equations (PDEs). For time-dependent problems, strong one-ste

Transversal Pooling Neural Networks

arXiv:2609.36237v1 Announce Type: new Abstract: Many learning tasks require stability to small transformations while retaining sensitivity to larger ones. We introduce transversal pooling neural netwo

Traverse: Learning When to Remember, Reset, and Redirect for Long-Horizon Web Search

arXiv:2609.37082v1 Announce Type: new Abstract: Long-horizon information-seeking agents often accumulate noisy or misleading context, causing early mistakes to persist and making recovery increasingly

agentsarxiv-cs-cl

Local Ai

Traversing the solution space of neural networks with Hessian Null Space Continuation

arXiv:2609.38081v1 Announce Type: new Abstract: On a single task, deep networks can learn many solutions, depending on their optimizer, training data, architecture, and hyperparameters. Many of these

local-aiarxiv-cs-lg

TReVS: Integrating Textual Relevance and Visual Saliency for Efficient Vision-Language Model Token Pruning

arXiv:2609.37581v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) excel at visual understanding and reasoning but often incur substantial inference costs due to the large number of visua

Triadic Linear Attention: Three-Dimensional Recurrent States for Long-Context Sequence Modeling

model-releasesarxiv-cs-cl

arXiv:2609.36529v1 Announce Type: cross Abstract: Recurrent neural networks (RNNs) compress the historical context into a memory state of fixed size, thus allowing for constant-time inference. The mem

Hardware

Trident: Unifying Guarded Dispatch and Host Execution for PyTorch Triton Workloads

arXiv:2609.37241v1 Announce Type: new Abstract: User-written Triton kernels enable high-performance GPU computation within PyTorch, but their end-to-end latency can remain dominated by host-side orche

hardwarearxiv-cs-lg

TTMark: Pairwise Distortion-Free Watermarking Beyond Single-Token Entropy

arXiv:2609.36372v1 Announce Type: cross Abstract: Distortion-free watermarking enables reliable attribution of machine-generated text while preserving output distribution. However, existing methods op

researcharxiv-cs-cl

UGO: Unified Architecture for General Multi-Object Tracking by Segmentation

arXiv:2609.37339v1 Announce Type: new Abstract: General multi-object tracking (GMOT) tracks all instances of a user-specified category from a single first-frame exemplar. Prior work relies on bounding

researcharxiv-cs-cv

UltRAG: a Universal Simple Scalable Recipe for Knowledge Graph RAG

model-releasesarxiv-cs-cl

arXiv:2603.28773v2 Announce Type: replace-cross Abstract: Large language models (LLMs) frequently generate confident yet factually incorrect content when used for language generation (a phenomenon oft

UltraMatch: Transport Path Routing for Ultra-Fast and Memory-Efficient Image Matching

arXiv:2609.36980v1 Announce Type: new Abstract: Despite recent advances in accuracy and efficiency, coarse matching remains an indispensable yet costly stage in existing semi-dense matchers due to den

researcharxiv-cs-cv

Tutorials

UNBIND: UNlearning By INference-time Directional Steering for Code LLMs

arXiv:2609.35913v1 Announce Type: cross Abstract: Code large language models acquire programming capabilities from large code corpora, but can also memorize implementations that later require removal.

tutorialsarxiv-cs-ai

Understanding Decision-Making Mechanisms in Neural Routing Solvers

arXiv:2609.36063v1 Announce Type: cross Abstract: Neural Combinatorial Optimization (NCO) has achieved strong empirical success, yet the internal mechanisms driving model decisions remain largely unex

Understanding LLM Parameter Update Sparsity through the Lens of Fisher

model-releasesarxiv-cs-lg

arXiv:2609.36262v1 Announce Type: new Abstract: Recent studies have observed that parameter changes during language-model post-training can be concentrated in a small subset of coordinates. This pheno

Understanding Private Evolution as Learning-Augmented Clustering

arXiv:2609.36678v1 Announce Type: new Abstract: Private Evolution (PE) is a differentially private algorithm for synthetic data generation. While it can be viewed as a Wasserstein learning algorithm,

Local Ai

UniAfford: Token-Routed Multitask Learning for Generalizable 2D-3D Affordance Perception

arXiv:2609.37264v1 Announce Type: cross Abstract: Affordance perception aims to localize actionable regions supporting embodied interaction, yet 2D and 3D affordance grounding have evolved as separate

local-aiarxiv-cs-ai

UniBuild: Unified Building Mapping From Multi-Source Optical Remote Sensing Imagery With Detail Decoding and Geometry Regularization

model-releasesarxiv-cs-cv

arXiv:2609.37031v1 Announce Type: new Abstract: Building extraction from optical remote sensing (RS) imagery is fundamental to urban mapping, yet existing methods are often dataset-specific and genera

UnlearningSoup: Is Repeated Tuning Necessary for Large Language Model Unlearning?

model-releasesarxiv-cs-lg

arXiv:2609.37076v1 Announce Type: new Abstract: Large language models trained on vast corpora inherently risk memorizing harmful content that may later re-emerge in their outputs. To mitigate this iss

Unlocking the Critic: Reward-Free Policy Optimization for LLM Post-Training

arXiv:2609.37119v1 Announce Type: cross Abstract: Recent approaches to reinforcement learning (RL) post-training for large language models increasingly remove the critic to reduce training instability

UpliftMem: Learning Set-Level Uplift for Agent Memory Retrieval

arXiv:2609.36805v1 Announce Type: new Abstract: Large language model (LLM) agents reuse external memory to guide new tasks, but effective retrieval requires learning which memory sets improve executio

Applications

Urgent Actions Go First: Urgency-Aware Denoising for Real-Time VLA Control

arXiv:2609.37772v1 Announce Type: new Abstract: Diffusion and flow-matching Vision-Language-Action (VLA) policies generate action chunks through iterative denoising, incurring substantial inference la

applicationsarxiv-cs-ro

UserProxyBench: Evaluating LLM User Simulators for Agent Benchmarks and Training

arXiv:2609.38043v1 Announce Type: new Abstract: Interactive agent benchmarks and multi-turn reinforcement learning increasingly place a second language model in the role of the user. This simulated us

V-Engram: Trigger-Indexed External Memory for Modular Text-to-Image Personalization

arXiv:2609.37198v1 Announce Type: new Abstract: Pretrained text-to-image models contain broad visual knowledge, yet they cannot reliably acquire or refine a specific visual identity from only a few re

V-JEPA Policy: Building Effective World-Action Models on Predictive Visual Latents

arXiv:2609.37250v1 Announce Type: cross Abstract: World-action models (WAMs) couple future visual-state prediction with action generation. By adapting video generators or image-editing models pretrain

V2X-WAM: A Cooperative World Action Model for End-to-End Autonomous Driving

arXiv:2609.37098v1 Announce Type: cross Abstract: Vehicle-infrastructure cooperation can complement onboard sensing with broader and more informative observations of the traffic environment, providing

safetyarxiv-cs-cv

VAA-CSEC: Vote-guided Advantage Allocation for Chinese Semantic Error Correction

arXiv:2609.36804v1 Announce Type: cross Abstract: Chinese Semantic Error Correction (CSEC) targets semantic errors in Chinese text, which are typically more subtle and complex than spelling and gramma

VACE: Validation-Gated Alternating Co-Evolution of Agent Models and Harnesses

arXiv:2609.37105v1 Announce Type: cross Abstract: Language model agents can be improved by updating their model weights or refining the harness that guides task execution. These components are coupled

Variational Augmented Invertible Koopman Autoencoder for probabilistic time series forecasting

arXiv:2609.37435v1 Announce Type: new Abstract: Neural Koopman autoencoder models have been shown to successfully build a latent embedding with linear dynamics for arbitrary dynamical systems, enablin

Variational Mixtures and Multi-Marginal Flow Matching: Advancing Statistical Inference with Biological Applications

arXiv:2609.36911v1 Announce Type: new Abstract: In this thesis I develop methods for statistical inference when the distributions arising from complex biological systems are multi-modal, geometrically

VehicleArena: A Realistic Urban Environment for Multi-Agent Driving

model-releasesarxiv-cs-cl

arXiv:2609.35916v1 Announce Type: cross Abstract: Real-world embodied agents often pursue independent objectives within a shared physical environment, where their actions can alter the conditions face

VeriWeave Govern: Evidence-Gated Deterministic Runtime Governance for Enterprise AI Agents

arXiv:2609.37457v1 Announce Type: new Abstract: Enterprise artificial-intelligence agents increasingly call tools, modify infrastructure, and process protected data, creating a need to separate action