Search: “models”

Filesystem-Based Memory for LLM Agents: Organization, Evolution, and Sustainability

arXiv:2607.26637v1 Announce Type: new Abstract: Deployed LLM agents increasingly keep their long-term memory as a filesystem: a directory tree of markdown files that the agent itself reads, writes, an

agentsarxiv-cs-cl

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FreqForcing: Autoregressive Long Video Generation via Spectral Self-Anchoring

DGX agent

arXiv:2607.27110v1 Announce Type: new Abstract: Autoregressive video diffusion models enable real-time streaming video generation. However, errors introduced during self-rollout accumulate over long h

researcharxiv-cs-cv

30 Jul 2026

From Representations to Behaviors: Exploring the Person-Situation-Behavior Triad in LLMs

arXiv:2607.26853v1 Announce Type: new Abstract: Human personality theories characterize traits not as isolated attributes captured by a single score, but as stable individual tendencies expressed thro

From Spatial Semantics to Temporal Context: Leveraging Gaze Trajectory for Weakly Supervised Medical Image Segmentation

arXiv:2607.26542v1 Announce Type: new Abstract: Medical image segmentation heavily depends on labor-intensive and time-consuming pixel-level annotations. Eye tracking offers a cost-effective solution

Generation or Judgement? A Paradigm Perspective on LLM-Based Emotion-Cause Pair Extraction in Conversation

arXiv:2607.26967v1 Announce Type: new Abstract: Emotion-cause pair extraction in conversation (ECPEC) identifies utterance pairs in which one utterance causes an emotion expressed in another. Recent L

HERMES: A Hybrid Ensemble for Head-and-Neck Tumor Segmentation, TN Staging, and Recurrence-Free Survival on PET/CT

arXiv:2607.26498v1 Announce Type: new Abstract: We present HERMES (Hybrid Ensemble for Radiotherapy-target segmentation, Malignancy staging, and Event-free Survival), a single containerized algorithm

Incast-Free MoE Rate-Based Scheduling

arXiv:2607.26340v1 Announce Type: cross Abstract: Mixture of Experts (MoE) architectures have become key to large language models; however, their typical round-robin (RR) scheduling introduces signifi

Kinetic Mining in Context: Few-Shot Action Synthesis via Text-to-Motion Distillation

arXiv:2512.11654v3 Announce Type: replace Abstract: The acquisition cost for large, annotated motion datasets remains a critical bottleneck for skeletal-based Human Activity Recognition (HAR). Althoug

Latent-IM: Latent Interaction Management for Speech LLMs

arXiv:2607.26928v1 Announce Type: new Abstract: Classical spoken dialogue systems often separated dialogue management from response realization: a policy selected the next dialogue action, and a gener

safetyarxiv-cs-cl

LLM-Grounded Dynamic Task Planning with Hierarchical Temporal Logic for Human-Aware Multi-Robot Handover

arXiv:2602.09472v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) enable non-experts to specify open-world multi-robot tasks, but the generated plans are often kinematically infea

Making Implicit Premises Explicit in Logical Understanding of Enthymemes

arXiv:2603.06114v2 Announce Type: replace Abstract: Real-world arguments in text and dialogues are normally enthymemes (i.e. some of their premises and/or claims are implicit). Natural language proces

applicationsarxiv-cs-cl

On the robustness of noisy solutions in non-convex neural networks

arXiv:2607.27000v1 Announce Type: cross Abstract: Optimization in non-convex neural network models is strongly influenced by the geometry of the solution space: sparse, isolated, point-like clusters a

Pangram 4 Technical Report

arXiv:2607.27183v1 Announce Type: new Abstract: We present Pangram 4, the latest deep-learning-based AI-text classification model from Pangram Labs. We achieve an AUROC of 0.9916 with a false positive

Particle-Filtering-based Latent Diffusion for Inverse Problems

arXiv:2408.13868v2 Announce Type: replace Abstract: Current strategies for solving image-based inverse problems apply latent diffusion models to perform posterior sampling.However, almost all approach

PIKS: Universal Physics-Informed Kernel Methods

arXiv:2607.27062v1 Announce Type: cross Abstract: Physics-informed machine learning incorporates physical principles --often expressed via differential operators-- into data-driven models. While physi

RA-Det: Towards Universal Detection of AI-Generated Images via Robustness Asymmetry

arXiv:2603.01544v2 Announce Type: replace Abstract: Recent image generators produce photo-realistic content that undermines the reliability of downstream recognition systems. As visual appearance cues

Recover, Decode, Reguard: Guard-Agnostic Defense Amplification againstEncoded VLM Jailbreaks

arXiv:2607.26574v1 Announce Type: cross Abstract: Safety classifiers ('guards') are the dominant black-box defense for vision-language models, yet they judge an input's surface form, not its meaning:

safetyarxiv-cs-lg

Robust RPC Bundle Adjustment for Multi-Date Satellite Imagery with Season-Invariant Correspondences

arXiv:2607.26973v1 Announce Type: new Abstract: Accurate refinement of Rational Polynomial Camera (RPC) models is essential for high-quality satellite image geolocation. In ground control point (GCP)-

SeasonStereo: Robust Dense Stereo Matching for Multi-Date Satellite Imagery via Generative AI

arXiv:2607.27139v1 Announce Type: new Abstract: Accurate 3D reconstruction from satellite imagery typically relies on near-simultaneous stereo pairs, limiting its applicability to diachronic settings

SENSE: Efficient EEG-to-Text via Privacy-Preserving Semantic Retrieval

arXiv:2603.17109v2 Announce Type: replace Abstract: Decoding brain activity into natural language is a major challenge in AI with important applications in assistive communication, neurotechnology, an

local-aiarxiv-cs-lg

SG-CoT: An Ambiguity-Aware Robotic Planning Framework using Scene Graph Representations

arXiv:2603.18271v3 Announce Type: replace Abstract: Ambiguity poses a major challenge to large language models (LLMs) used as robotic planners. In this letter, we present Scene Graph-Chain-of-Thought

agentsarxiv-cs-ro

Shape-Based Inductive Bias for Glioma Grading from Tumor Contours

arXiv:2607.26090v1 Announce Type: cross Abstract: Glioma grading from tumor contours is often treated as a pixel problem even when the signal of interest is shape. We align closed contours with a func

safetyarxiv-cs-cv

Single-Beat Cuffless Blood Pressure Estimation Using Ear-PPG and ECG with a Lightweight Hybrid Learning Framework

arXiv:2607.27076v1 Announce Type: new Abstract: Continuous cuffless blood pressure (BP) monitoring remains challenging due to motion artifacts, physiological variability, and the limited robustness of

applicationsarxiv-cs-lg

SkillRise: Agentic Reinforcement Learning for Cross-Task Skill Evolution

arXiv:2607.26784v1 Announce Type: new Abstract: Large language model agents often encounter related yet distinct tasks that share reusable solution patterns. Yet standard agentic reinforcement learnin

safetyarxiv-cs-lg

A GAN-Based Framework for Robust Data Synthesis in Satellite Internet Observations

arXiv:2607.24790v1 Announce Type: new Abstract: Low-Earth orbit (LEO) satellite Internet has become an important infrastructure for enabling ubiquitous connectivity to align with the International Tel

applicationsarxiv-cs-ai

AdaKP: Online Adaptive Knowledge-Point Selection for Reasoning-Oriented Reinforcement Learning

arXiv:2607.24833v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards is a powerful paradigm for eliciting reasoning in large language models, yet it suffers from severe rewar

safetyarxiv-cs-ai

AMRD: Adaptive Multi-Teacher Relational Distillation for Lightweight Speech Emotion Recognition

arXiv:2607.25289v1 Announce Type: new Abstract: On-device speech emotion recognition (SER) is critical for real-time applications, yet large self-supervised models that excel at SER are too costly for

local-aiarxiv-cs-lg

An Embarrassingly Simple Rule-based Visiting Circulation Approach to Trip Destination Prediction

arXiv:2607.25751v1 Announce Type: new Abstract: In this paper, we propose the Rule-based Visiting Circulation (RVC) model in tackling the challenge in the IEEE Big Data Cup 2022: Trip Destination Pred

Are the High-weight Neurons the Important Ones in Image Classification Neural Networks?

arXiv:2607.25529v1 Announce Type: new Abstract: As neural network models for image classification advance, neurons play critical roles in pruning, backdoor defense, and interpretability. Yet existing

Tutorials

Bi-Level Collaborative Learning for Few-Shot Scribble-Supervised Medical Image Segmentation

arXiv:2607.25432v1 Announce Type: new Abstract: Scribble annotations offer an efficient alternative to costly pixel-wise labeling for medical image segmentation, yet in real clinical scenarios, scribb

tutorialsarxiv-cs-cv

CAST: Game Solvers as Turn-Level Teachers for LLM Agents

arXiv:2607.25308v1 Announce Type: cross Abstract: Training large language models (LLMs) to act in long-horizon games is a promising step toward generalist decision-making, yet reinforcement learning w

safetyarxiv-cs-ai

Constrained CTC Decoding for Efficient Diacritic Restoration

arXiv:2607.18946v2 Announce Type: replace Abstract: In this work, we address diacritic restoration for Arabic speech transcripts. Most speech data are undiacritized, limiting the ability of modeling f

Controllable LLM Reasoning via Sparse Autoencoder-Based Steering

arXiv:2601.03595v2 Announce Type: replace Abstract: Large Reasoning Models (LRMs) exhibit human-like cognitive reasoning strategies (eg backtracking, cross-verification) during the reasoning process,

Cyber-Capable AI Agents: Vulnerabilities, Evaluation Containment, and Defensive Response

arXiv:2607.25379v1 Announce Type: new Abstract: Cyber-capable AI agents combine language models with tools, memory, and execution en- vironments to perform multi-step offensive-security tasks. Existin

DynaBridge: Dynamic Summary-Guided Cross-Task Multimodal Fusion for DASS-Structured Mental Health Assessment

arXiv:2607.25679v1 Announce Type: cross Abstract: Multimodal behavioral analysis offers a scalable approach to assessing depression, anxiety, and stress, yet generic fusion models often ignore the psy

FinAbstain: Uncertainty-Calibrated Multimodal RAG for Selective Financial Forecasting

arXiv:2607.24875v1 Announce Type: new Abstract: Large language models (LLMs) can synthesize financial narratives but may express high confidence when evidence is sparse, stale, or contradictory. This

From Naive RAG to Deep Agentic Retrieval: An Evolving Context Engineering Pipeline for Regulatory Compliance

arXiv:2607.24791v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) is the dominant paradigm for applying large language models (LLMs) to enterprise document corpora, yet naive impl

Guardians and Offenders: A Survey on Harmful Content Generation and Safety Mitigation of LLM

arXiv:2508.05775v3 Announce Type: replace Abstract: Large Language Models (LLMs) have revolutionized content creation across digital platforms, offering unprecedented capabilities in natural language

safetyarxiv-cs-cl

HiSkill: Empowering LLM Agents with Hierarchical Skill Graphs

arXiv:2607.25853v1 Announce Type: new Abstract: Skills have become an important abstraction for enabling large language model (LLM) agents to reuse past experience in long-horizon interactive tasks. H

How Often Should a Recommender Call an LLM? Value-Weighted Routing, Monitoring, and Seasonal Robustness

arXiv:2607.25068v1 Announce Type: new Abstract: Routing decisions between a cheap heuristic and an expensive large language model (LLM) are typically framed as a difficulty problem: send the hard case

applicationsarxiv-cs-ai

JKO-RAG: Distributional Retrieval as Wasserstein Free-Energy Gradient Flow

arXiv:2607.24776v1 Announce Type: cross Abstract: RAG pipelines return a ranked list of passages. We argue this is a mismatch: the downstream language model conditions on a set, and the selection prob

Knowledge-Guided Multimodal Reasoning over Interacting Streams for Video-Level Ambivalence and Hesitancy Recognition

arXiv:2607.25961v1 Announce Type: cross Abstract: Ambivalence and hesitancy (A/H) are conflicting affective states that precede the delay or abandonment of health behaviour change. Recognition of A/H

Language as a Material Interface for Creative LLM Interaction

arXiv:2607.24753v1 Announce Type: cross Abstract: Although directive prompting is the predominant way to interact with Large Language Models (LLMs), many creative practices rely on language that is op

MARS: Multi-Agent Re-ranking for Repeat-Order Food Delivery Recommendation

arXiv:2607.25420v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in recommender systems, but it is often unclear how much performance can be obtained from strong pr

local-aiarxiv-cs-ai

Tutorials

Preliminary Guidelines for Using and Evaluating GenAI Tools to Support Systematic Literature Reviews

arXiv:2607.24991v1 Announce Type: cross Abstract: Context: Generative AI (GenAI) and Large Language Models (LLMs) are increasingly used for academic tasks in software engineering and beyond, including

tutorialsarxiv-cs-ai

ProcAgent: An Agentic Framework for Procedural Task Guidance on Edge with Human-in-the-Loop

arXiv:2607.24770v1 Announce Type: new Abstract: Procedural tasks such as furniture assembly and home repair impose substantial cognitive demands because users must interpret instructions, track task p

local-aiarxiv-cs-ai

Retrieval, not hallucinations, will be the limiting factor for LLM-based clinical AI tools

arXiv:2607.24793v1 Announce Type: cross Abstract: Discussions around large language model (LLM) errors in clinical artificial intelligence (AI) generally center around precision errors like hallucinat

Right-sizing Recommendations (RSR): Cloud Workload Conformal Prediction for Virtual Machines in Data Center Operations

arXiv:2607.24773v1 Announce Type: new Abstract: Managing cloud infrastructure efficiently, especially in environments of large cloud providers or hyperscalers, requires optimizing the use of physical