Search: “models”

Shapley-based Data Valuation for LLM Alignment via Sequential Preference Optimization

arXiv:2512.15765v3 Announce Type: replace Abstract: Data valuation is a natural framework for understanding which preference datasets matter most when aligning a Large Language Model (LLM) using multi

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Research

Shifting from Discrete to Continuous Reference Data: QSM-Derived Horizontal Tree Biomass Distribution for Deep Learning Biomass Estimation

DGX agent

arXiv:2607.05260v1 Announce Type: cross Abstract: Conventional modeling approaches for LiDAR-based above-ground biomass (AGB) estimation rely on discrete plot-level inventory aggregates. This methodol

researcharxiv-cs-ai

7 Jul 2026

Applications

Shortcut Learning in Legal Judgment Prediction: Empirical Evidence from the UK Employment Tribunal

arXiv:2607.04261v1 Announce Type: new Abstract: Current Legal Judgment Prediction (LJP) is constrained by its reliance on post-hoc judicial materials, increasing the likelihood that models perform ret

applicationsarxiv-cs-ai

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses

arXiv:2510.15476v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly used as interfaces to information, code, and real-world services, making prompt-level security f

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Agents

The Changing Role of Symbolic Methods in Artificial Intelligence

arXiv:2607.05168v1 Announce Type: new Abstract: Why do intelligent systems need to perform explicit symbolic reasoning? Computer science has traditionally regarded symbolic reasoning as a defining com

agentsarxiv-cs-ai

Toward Efficient Agents: Memory, Tool learning, and Planning

arXiv:2601.14192v2 Announce Type: replace Abstract: Recent years have witnessed increasing interest in extending large language models into agentic systems. While the effectiveness of agents has conti

Unsupervised Features Mining via Activation Geometry

arXiv:2607.04222v1 Announce Type: new Abstract: Interpretability methods aim to reveal the features represented inside large language models (LLMs). Many existing methods begin with labeled examples o

AbsoluteDegradation: A Physics-Inspired Synthetic Film-Degradation Pipeline and Archival Film Restoration Benchmark

arXiv:2607.02131v1 Announce Type: cross Abstract: Restoring archival film remains a fundamentally challenging problem due to the absence of paired training data and the lack of standardized evaluation

Activation Steering for Aligned Open-ended Generation without Sacrificing Coherence

arXiv:2604.08169v2 Announce Type: replace Abstract: Alignment in LLMs is more brittle than commonly assumed: misalignment can be induced by adversarial prompts, benign fine-tuning, emergent misalignme

Composite Reward Design in PPO-Driven Adaptive Filtering

arXiv:2506.06323v2 Announce Type: replace-cross Abstract: Model-free and reinforcement learning-based adaptive filtering methods are gaining traction for denoising in dynamic, non-stationary environme

ContextSniper: AntTrail's Token-Efficient Code Memory for Repository-Level Program Repair

arXiv:2607.01916v1 Announce Type: new Abstract: Large language model agents can repair real repository issues, but they often spend large context budgets on whole-file reads, broad searches, and long

Denser neq Better: Limits of On-Policy Self-Distillation for Continual Post-Training

arXiv:2607.01763v1 Announce Type: cross Abstract: Continual post-training enables foundation models to acquire new knowledge while preserving existing capabilities. Recent work suggests that on-policy

Do Newer Lightweight CNNs Perform Better Under Resource Constraints? A Controlled Multigenerational Study of Architecture, Initialization, Training Budget, and Efficiency

arXiv:2607.01984v1 Announce Type: cross Abstract: Newer lightweight convolutional neural networks are often presented as improving predictive performance and deployment efficiency, but such claims req

EPnG: Adaptive Expert Prune-and-Grow for Parameter-Efficient MoE Fine-tuning

arXiv:2607.01789v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models scale efficiently but remain costly to adapt due to redundant experts and uniform parameter allocation. Existing param

Introduction to Transformers: an NLP Perspective

arXiv:2311.17633v2 Announce Type: replace-cross Abstract: Transformers have dominated empirical machine learning models of natural language processing. In this paper, we introduce basic concepts of Tr

LACUNA: A Testbed for Evaluating Localization Precision for LLM Unlearning

arXiv:2607.02513v1 Announce Type: cross Abstract: LLMs memorize sensitive training data, including personally identifiable information (PII), creating a pressing need for reliable post hoc removal met

MMIR-TCM: Memory-Integrated Multimodal Inference and Retrieval for TCM Clinical Decision Support

arXiv:2607.01814v1 Announce Type: new Abstract: Traditional Chinese Medicine (TCM) diagnosis, particularly through tongue inspection, faces persistent challenges in subjectivity and reproducibility. T

One More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspective

arXiv:2607.02292v1 Announce Type: new Abstract: Neural quantum states (NQS) provide a flexible and scalable framework for approximating quantum many-body wavefunctions. Among NQS parameterizations, au

Overthink-Triggered Slowdown Attacks on LVLM-Based Robotic Systems

arXiv:2607.01518v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have been increasingly integrated into robotic systems. However, these models may exhibit overthinking behaviors,

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Applications

PACE: A Neuro-Symbolic Framework for Plausible and Actionable Counterfactual Explanations

arXiv:2607.01306v1 Announce Type: new Abstract: Counterfactual explanations explain machine learning predictions by identifying minimal input changes that would alter a model's decision. Although many

applicationsarxiv-cs-ai

Phonikud: Overcoming Phonetic Underspecification for Hebrew Text-To-Speech

arXiv:2506.12311v4 Announce Type: replace Abstract: Text-to-speech (TTS) for Modern Hebrew is challenged by the language's orthographic complexity, with existing solutions ignoring underspecified phon

Population-Scale Segmentation of Penile Tissue in DIXON MRI using Deep Learning for Quantitative Phenotyping in Male Reproductive Health

arXiv:2607.02127v1 Announce Type: cross Abstract: Penile measurement is clinically relevant across male reproductive and urogenital health, including conditions such as micropenis, congenital and endo

Scaling Trends for Lie Detector Oversight in Preference Learning

arXiv:2607.01567v1 Announce Type: new Abstract: Deceptive behavior in LLMs is costly to monitor and prevent, motivating approaches such as Scalable Oversight via Lie Detectors (SOLiD) (Cundy & Gleave,

Local Ai

Self-explainable Operator Learning for Discovering Spatial Patterns in Functional Data

arXiv:2607.02203v1 Announce Type: new Abstract: Operator learning has emerged as a powerful tool for modeling complex physical systems in functional spaces. However, their neural network-based archite

local-aiarxiv-cs-lg

Separating Expert Retention from Autonomous Source Inference in Raw-ECG-Replay-Free Continual ECG Deployment

arXiv:2607.01674v1 Announce Type: new Abstract: In multi-source ECG deployment, models may need to incorporate new data sources when earlier raw ECGs cannot be retained or replayed. Freezing a pretrai

SPARCLE: SPeaker-aware Aligned Representations via Contrastive Language Embeddings

arXiv:2607.01238v1 Announce Type: cross Abstract: Recent advances in speech synthesis have shifted from phoneme representations to direct grapheme modeling. While phonemes address the one-to-many mapp

Spec-AUF: Accept-Until-Fail Training under Train-Inference Misalignment for Masked Block Drafters

arXiv:2607.01893v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive generation by drafting a block of tokens that the target model verifies left-to-right, committing only t

SPLIT: Cross-Lingual Empathy and Cultural Grounding in English and Ukrainian LLM Responses

arXiv:2607.02049v1 Announce Type: cross Abstract: Large Language Models are increasingly deployed in emotional-support contexts and crisis-related situations. Nevertheless, their cross-lingual abiliti

Token Geometry

arXiv:2607.01455v1 Announce Type: cross Abstract: Language models learn continuous programs over discrete symbols, with the embedding table and LM-head acting as the read/write interface between them.

UA-ChatDev: Uncertainty-Aware Multi-Agent Collaboration for Reliable Software Development

arXiv:2607.02186v1 Announce Type: new Abstract: Software development is a complex task that demands cooperation among agents with diverse roles. Large language models (LLMs) have enabled autonomous mu

A Filtered Mixture-of-Generators for Fully Synthetic Survival Training

arXiv:2607.00127v1 Announce Type: new Abstract: Survival analysis models time-to-event data, but in clinical settings training data are costly and scarce: events accrue over years of follow-up, cohort

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AGE: Adaptive-masking for Graph Embedding in Graph Retrieval-Augmented Generation

arXiv:2607.00052v1 Announce Type: cross Abstract: GraphRAG is an extension of retrieval-augmented generation (RAG) that supports large language models (LLMs) by referring to graph-structured data as e

An LLM-Based Framework for Intent-Driven Network Topology Design

arXiv:2607.00292v1 Announce Type: cross Abstract: Designing deployable and resilient network topologies from natural language requirements remains a challenging problem in network automation. This wor

Local Ai

Beyond Perplexity: A Behavioral Evaluation Framework for Deployment-Memory Claims in LLM Test-Time Training

arXiv:2607.00368v1 Announce Type: new Abstract: Large language model test-time training (TTT) is often evaluated through local proxy metrics: models are updated on recent tokens, retrieved context, ta

local-aiarxiv-cs-cl

Can Agents Generalize to the Open World? Unveiling the Fragility of Static Training in Tool Use

arXiv:2607.01084v1 Announce Type: new Abstract: While Large Language Model (LLM) agents demonstrate proficiency in static benchmarks, their deployment in real-world scenarios is hindered by the dynami

Does Your ViT Still Need U-Net for Segmentation?

model-releasesarxiv-cs-cv

arXiv:2607.00223v1 Announce Type: new Abstract: Medical image segmentation is dominated by U-Net-style encoder-decoder architectures. Vision Transformers (ViTs) overcome the limited receptive field of

Flow-Map GRPO: Reinforcement Learning for Few-Step Flow-Map Generators via Anchored Stochastic Composition

arXiv:2607.00535v1 Announce Type: cross Abstract: Few-step flow-map generators, such as consistency models and MeanFlow, accelerate sampling by directly learning long-range transport maps between nois

FLYNN: Robust Neural Network for Robot Navigation using Fly Brain Topology

arXiv:2607.00025v1 Announce Type: cross Abstract: While deep learning models achieve state-of-the-art performance in complex tasks, they remain brittle when faced with new environments or sensory depr

GMO-E^2DIT: Grounded Multi-Operation Editing for E-Commerce Images

model-releasesarxiv-cs-cv

arXiv:2607.00920v1 Announce Type: new Abstract: Real-world e-commerce image editing often requires multiple, localized, and auditable operations rather than global restyling. This compositional nature

GPTKB v1.5: A Massive Knowledge Base for Exploring Factual LLM Knowledge

arXiv:2507.05740v2 Announce Type: replace Abstract: Language models are powerful artifacts, yet their factual knowledge is still poorly understood, and inaccessible to ad-hoc browsing and scalable sta

GSRQ: Gain-Shape Residual Quantization for Sub-1-bit KV Cache

arXiv:2607.01065v1 Announce Type: new Abstract: The deployment of Large Language Models (LLMs) with extended context windows is increasingly constrained by the linear growth of Key-Value (KV) cache me

Homogenization of ell_2-Adversarial Training in High-Dimensions: Exact Dynamics under Stochastic Gradient Descent

arXiv:2607.00207v1 Announce Type: cross Abstract: We develop a framework for analyzing the learning dynamics of ell_2-adversarial training of single-index models on Gaussian mixtures in the high-dimen

Leveraging Multimodality for Real-Time Classification of Transients and Variables found by the Zwicky Transient Facility

arXiv:2607.00228v1 Announce Type: cross Abstract: Modern time-domain surveys such as the Zwicky Transient Facility (ZTF) generate hundreds of thousands of alerts each night, making real-time decisions

researcharxiv-cs-lg

Lost in the Tail: Addressing Geographic Imbalance in Urban Visual Place Recognition

arXiv:2607.00090v1 Announce Type: cross Abstract: Urban-scale Visual Place Recognition (VPR) aims to identify the geographic location of a query image by matching it against a geo-tagged database. Whi

LV-ROVER: Multi-Stream Tesseract Voting for Maltese Paragraph OCR

arXiv:2607.00250v1 Announce Type: new Abstract: Maltese has decent text corpora and pretrained language models, but, like many languages outside the handful with large OCR benchmarks, only a single kn

Message Passing Enables Efficient Reasoning

arXiv:2607.01077v1 Announce Type: new Abstract: While inference-time scaling has improved the reasoning abilities of large language models (LLMs), the need to generate long chains-of-thought (CoTs) is

researcharxiv-cs-cl

MMLoP: Multi-Modal Low-Rank Prompting for Efficient Vision-Language Adaptation

model-releasesarxiv-cs-cv

arXiv:2602.21397v2 Announce Type: replace Abstract: Prompt learning has become a dominant paradigm for adapting vision-language models (VLMs) such as CLIP to downstream tasks without modifying pretrai

Multi-Hypothesis Test-Time Adaptation to Mitigate Underspecification

arXiv:2607.00259v1 Announce Type: cross Abstract: Test-Time Adaptation (TTA) seeks to improve model robustness under distribution shifts by adapting parameters using unlabeled target data. However, in